International Journal of Imaging Systems and Technology最新文献

{"title":"ConvNext Mixer-Based Encoder Decoder Method for Nuclei Segmentation in Histopathology Images","authors":"Hüseyin Firat,&nbsp;Hüseyin Üzen,&nbsp;Davut Hanbay,&nbsp;Abdulkadir Şengür","doi":"10.1002/ima.23181","DOIUrl":"https://doi.org/10.1002/ima.23181","url":null,"abstract":"<p>Histopathology, vital in diagnosing medical conditions, especially in cancer research, relies on analyzing histopathology images (HIs). Nuclei segmentation, a key task, involves precisely identifying cell nuclei boundaries. Manual segmentation by pathologists is time-consuming, prompting the need for robust automated methods. Challenges in segmentation arise from HI complexities, necessitating advanced techniques. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have transformed nuclei segmentation. This study emphasizes feature extraction, introducing the ConvNext Mixer-based Encoder-Decoder (CNM-ED) model. Unlike traditional CNN based models, the proposed CNM-ED model enables the extraction of spatial and long context features to address the inherent complexities of histopathology images. This method leverages a multi-path strategy using a traditional CNN architecture as well as different paths focused on obtaining customized long context features using the ConvNext Mixer block structure that combines ConvMixer and ConvNext blocks. The fusion of these diverse features in the final segmentation output enables improved accuracy and performance, surpassing existing state-of-the-art segmentation models. Moreover, our multi-level feature extraction strategy is more effective than models using self-attention mechanisms such as SwinUnet and TransUnet, which have been frequently used in recent years. Experimental studies were conducted using five different datasets (TNBC, MoNuSeg, CoNSeP, CPM17, and CryoNuSeg) to analyze the performance of the proposed CNM-ED model. Comparisons were made with various CNN based models in the literature using evaluation metrics such as accuracy, AJI, macro F1 score, macro intersection over union, macro precision, and macro recall. It was observed that the proposed CNM-ED model achieved highly successful results across all metrics. Through comparisons with state-art-of models from the literature, the proposed CNM-ED model stands out as a promising advancement in nuclei segmentation, addressing the intricacies of histopathological images. The model demonstrates enhanced diagnostic capabilities and holds the potential for significant progress in medical research.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Deformation Vector Field Generation With Diffusion Models and Mamba-Based Network for Registration Performance Enhancement","authors":"Zengan Huang,&nbsp;Shan Gao,&nbsp;Xiaxia Yu,&nbsp;Liangjia Zhu,&nbsp;Yi Gao","doi":"10.1002/ima.23171","DOIUrl":"https://doi.org/10.1002/ima.23171","url":null,"abstract":"<p>Recent advancements in deformable image registration (DIR) have seen the emergence of supervised and unsupervised deep learning techniques. However, supervised methods are limited by the quality of deformation vector fields (DVFs), while unsupervised approaches often yield suboptimal results due to their reliance on indirect dissimilarity metrics. Moreover, both methods struggle to effectively model long-range dependencies. This study proposes a novel DIR method that integrates the advantages of supervised and unsupervised learning and tackle issues related to long-range dependencies, thereby improving registration results. Specifically, we propose a DVF generation diffusion model to enhance DVFs diversity, which could be used to facilitate the integration of supervised and unsupervised learning approaches. This fusion allows the method to leverage the benefits of both paradigms. Furthermore, a multi-scale frequency-weighted denoising module is integrated to enhance DVFs generation quality and improve the registration accuracy. Additionally, we propose a novel MambaReg network that adeptly manages long-range dependencies, further optimizing registration outcomes. Experimental evaluation of four public data sets demonstrates that our method outperforms several state-of-the-art techniques based on either supervised or unsupervised learning. Qualitative and quantitative comparisons highlight the superior performance of our approach.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142273012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid Convolutional Neural Network Model for the Classification of Multi-Class Skin Cancer","authors":"Ahmet Nusret Toprak,&nbsp;Ibrahim Aruk","doi":"10.1002/ima.23180","DOIUrl":"https://doi.org/10.1002/ima.23180","url":null,"abstract":"<p>Skin cancer is a significant public health issue, making accurate and early diagnosis crucial. This study proposes a novel and efficient hybrid deep-learning model for accurate skin cancer diagnosis. The model first employs DeepLabV3+ for precise segmentation of skin lesions in dermoscopic images. Feature extraction is then carried out using three pretrained models: MobileNetV2, EfficientNetB0, and DenseNet201 to ensure balanced performance and robust feature learning. These extracted features are then concatenated, and the ReliefF algorithm is employed to select the most relevant features. Finally, obtained features are classified into eight categories: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, melanocytic nevus, squamous cell carcinoma, and vascular lesion using the kNN algorithm. The proposed model achieves an <i>F</i>1 score of 93.49% and an accuracy of 94.42% on the ISIC-2019 dataset, surpassing the best individual model, EfficientNetB0, by 1.20%. Furthermore, the evaluation of the PH2 dataset yielded an <i>F</i>1 score of 94.43% and an accuracy of 94.44%, confirming its generalizability. These findings signify the potential of the proposed model as an expedient, accurate, and valuable tool for early skin cancer detection. They also indicate combining different CNN models achieves superior results over the results obtained from individual models.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142273010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the Complexities of COVID-19-Related Cardiac Complications: Enhancing Classification Accuracy With an Advanced Deep Learning Framework","authors":"Narjes Benameur,&nbsp;Ameni Sassi,&nbsp;Wael Ouarda,&nbsp;Ramzi Mahmoudi,&nbsp;Younes Arous,&nbsp;Mazin Abed Mohammed,&nbsp;Chokri ben Amar,&nbsp;Salam Labidi,&nbsp;Halima Mahjoubi","doi":"10.1002/ima.23189","DOIUrl":"https://doi.org/10.1002/ima.23189","url":null,"abstract":"<div>\u0000 \u0000 <p>The literature has widely described the interaction between cardiac complications and COVID-19. However, the diagnosis of cardiac complications caused by COVID-19 using Computed Tomography (CT) images remains a challenge due to the diverse clinical manifestations. To address this issue, this study proposes a novel configuration of Convolutional Neural Network (CNN) for detecting cardiac complications derived from COVID-19 using CT images. The main contribution of this work lies in the use of CNN techniques in combination with Long Short-Term Memory (LSTM) for cardiac complication detection. To explore two-class classification (COVID-19 without cardiac complications vs. COVID-19 with cardiac complications), 10 650 CT images were collected from COVID-19 patients with and without myocardial infarction, myocarditis, and arrhythmia. The information was annotated by two radiology specialists. A total of 0.926 was found to be the accuracy, 0.84 was the recall, 0.82 was the precision, 0.82 was the <i>F</i>₁-score, and 0.830 was the g-mean of the suggested model. These results show that the suggested approach can identify cardiac problems from COVID-19 in CT scans. Patients with COVID-19 may benefit from the proposed LSTM-CNN architecture's enhanced ability to identify cardiac problems.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142273011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intervertebral Cervical Disc Intensity (IVCDI) Detection and Classification on MRI Scans Using Deep Learning Methods","authors":"M. Fatih Erkoc,&nbsp;Hasan Ulutas,&nbsp;M. Emin Sahin","doi":"10.1002/ima.23174","DOIUrl":"https://doi.org/10.1002/ima.23174","url":null,"abstract":"<p>Radiologists manually interpret magnetic resonance imaging (MRI) scans for the detection of intervertebral cervical disc degeneration, which are often obtained in a primary care or emergency hospital context. The ability of computer models to work with pathological findings and aid in the first interpretation of medical imaging tests is widely acknowledged. Deep learning methods, which are commonly employed today in the diagnosis or detection of many diseases, show great promise in this area. For the detection and segmentation of intervertebral cervical disc intensity, we propose a Mask-RCNN-based deep learning algorithm in this study. The provided approach begins by creating an original dataset using MRI scans that were collected from Yozgat Bozok University. The senior radiologist labels the data, and three classes of intensity are chosen for the classification (low, intermediate, and high). Two alternative network backbones are used in the study, and as a consequence of the training for the Mask R-CNN algorithm, 98.14% and 96.72% mean average precision (mAP) values are obtained with the ResNet50 and ResNet101 architectures, respectively. Utilizing the five-fold cross-validation approach, the study is conducted. This study also applied the Faster R-CNN method, achieving a mAP value of 85.2%. According to the author's knowledge, no study has yet been conducted to apply deep learning algorithms to detect intervertebral cervical disc intensity in a patient population with cervical intervertebral disc degeneration. By ensuring accurate MRI image interpretation and effectively supplying supplementary diagnostic information to provide accuracy and consistency in radiological diagnosis, the proposed method is proving to be a highly useful tool for radiologists.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142234992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient-Residual Net—A Hybrid Neural Network for Automated Brain Tumor Detection","authors":"Jainy Sachdeva,&nbsp;Deepanshu Sharma,&nbsp;Chirag Kamal Ahuja,&nbsp;Arnavdeep Singh","doi":"10.1002/ima.23170","DOIUrl":"https://doi.org/10.1002/ima.23170","url":null,"abstract":"<div>\u0000 \u0000 <p>A multiscale feature fusion of Efficient-Residual Net is proposed for classifying tumors on brain Magnetic resonance images with solid or cystic masses, inadequate borders, unpredictable cystic and necrotic regions, and variable heterogeneity. Therefore, in this research, Efficient-Residual Net is proposed by efficaciously amalgamating features of two Deep Convolution Neural Networks—ResNet50 and EffficientNetB0. The skip connections in ResNet50 have reduced the chances of vanishing gradient and overfitting problems considerably thus learning of a higher number of features from input MR images. In addition, EffficientNetB0 uses a compound scaling coefficient for uniformly scaling the dimensions of the network such as depth, width, and resolution. The hybrid model has improved classification results on brain tumors with similar morphology and is tested on three internet repository datasets, namely, Kaggle, BraTS 2018, BraTS 2021, and real-time dataset from Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh. It is observed that the proposed system delivers an overall accuracy of 96.40%, 97.59%, 97.75%, and 97.99% on the four datasets, respectively. The proposed hybrid methodology has given assuring results of 98%–99% of other statistical such parameters as precision, negatively predicted values, and F1 score. The cloud-based web page is also created using the Django framework in Python programming language for accurate prediction and classification of different types of brain tumors.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dermo-Optimizer: Skin Lesion Classification Using Information-Theoretic Deep Feature Fusion and Entropy-Controlled Binary Bat Optimization","authors":"Tallha Akram,&nbsp;Anas Alsuhaibani,&nbsp;Muhammad Attique Khan,&nbsp;Sajid Ullah Khan,&nbsp;Syed Rameez Naqvi,&nbsp;Mohsin Bilal","doi":"10.1002/ima.23172","DOIUrl":"https://doi.org/10.1002/ima.23172","url":null,"abstract":"<div>\u0000 \u0000 <p>Increases in the prevalence of melanoma, the most lethal form of skin cancer, have been observed over the last few decades. However, the likelihood of a longer life span for individuals is considerably improved with early detection of this malignant illness. Even though the field of computer vision has attained a certain level of success, there is still a degree of ambiguity that represents an unresolved research challenge. In the initial phase of this study, the primary objective is to improve the information derived from input features by combining multiple deep models with the proposed Information-theoretic feature fusion method. Subsequently, in the second phase, the study aims to decrease the redundant and noisy information through down-sampling using the proposed entropy-controlled binary bat selection algorithm. The proposed methodology effectively maintains the integrity of the original feature space, resulting in the creation of highly distinctive feature information. In order to obtain the desired set of features, three contemporary deep models are employed via transfer learning: Inception-Resnet V2, DenseNet-201, and Nasnet Mobile. By combining feature fusion and selection techniques, we may effectively fuse a significant amount of information into the feature vector and subsequently remove any redundant feature information. The effectiveness of the proposed methodology is supported by an evaluation conducted on three well-known dermoscopic datasets, specifically <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>PH</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{PH}}^2 $$</annotation>\u0000 </semantics></math>, ISIC-2016, and ISIC-2017. In order to validate the proposed approach, several performance indicators are taken into account, such as accuracy, sensitivity, specificity, false negative rate (FNR), false positive rate (FPR), and F1-score. The accuracies obtained for all datasets utilizing the proposed methodology are 99.05%, 96.26%, and 95.71%, respectively.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Perceptual Constrained cycleGAN With Attention Mechanisms for Unsupervised MR-to-CT Synthesis","authors":"Ruiming Zhu,&nbsp;Xinliang Liu,&nbsp;Mingrui Li,&nbsp;Wei Qian,&nbsp;Yueyang Teng","doi":"10.1002/ima.23169","DOIUrl":"https://doi.org/10.1002/ima.23169","url":null,"abstract":"<div>\u0000 \u0000 <p>Radiotherapy treatment planning (RTP) requires both magnetic resonance (MR) and computed tomography (CT) modalities. However, conducting separate MR and CT scans for patients leads to misalignment, increased radiation exposure, and higher costs. To address these challenges and mitigate the limitations of supervised synthesis methods, we propose a novel unsupervised perceptual attention image synthesis model based on cycleGAN (PA-cycleGAN). The innovation of PA-cycleGAN lies in its model structure, which incorporates dynamic feature encoding and deep feature extraction to improve the understanding of image structure and contextual information. To ensure the visual authenticity of the synthetic images, we design a hybrid loss function that incorporates perceptual constraints using high-level features extracted by deep neural networks. Our PA-cycleGAN achieves notable results, with an average peak signal-to-noise ratio (PSNR) of 28.06, structural similarity (SSIM) of 0.95, and mean absolute error (MAE) of 46.90 on a pelvic dataset. Additionally, we validate the generalization of our method by conducting experiments on an additional head dataset. These experiments demonstrate that PA-cycleGAN consistently outperforms other state-of-the-art methods in both quantitative metrics and image synthesis quality.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SSANet—Novel Residual Network for Computer-Aided Diagnosis of Pulmonary Nodules in Chest Computed Tomography","authors":"Yu Gu,&nbsp;Jiaqi Liu,&nbsp;Lidong Yang,&nbsp;Baohua Zhang,&nbsp;Jing Wang,&nbsp;Xiaoqi Lu,&nbsp;Jianjun Li,&nbsp;Xin Liu,&nbsp;Dahua Yu,&nbsp;Ying Zhao,&nbsp;Siyuan Tang,&nbsp;Qun He","doi":"10.1002/ima.23176","DOIUrl":"https://doi.org/10.1002/ima.23176","url":null,"abstract":"<div>\u0000 \u0000 <p>The manifestations of early lung cancer in medical imaging often appear as pulmonary nodules, which can be classified as benign or malignant. In recent years, there has been a gradual application of deep learning-based computer-aided diagnosis technology to assist in the diagnosis of lung nodules. This study introduces a novel three-dimensional (3D) residual network called SSANet, which integrates split-based convolution, shuffle attention, and a novel activation function. The aim is to enhance the accuracy of distinguishing between benign and malignant lung nodules using convolutional neural networks (CNNs) and alleviate the burden on doctors when interpreting the images. To fully extract pulmonary nodule information from chest CT images, the original residual network is expanded into a 3D CNN structure. Additionally, a 3D split-based convolutional operation (SPConv) is designed and integrated into the feature extraction module to reduce redundancy in feature maps and improve network inference speed. In the SSABlock part of the proposed network, ACON (Activated or Not) function is also introduced. The proposed SSANet also incorporates an attention module to capture critical characteristics of lung nodules. During the training process, the PolyLoss function is utilized. Once SSANet generates the diagnosis result, a heatmap displays using Score-CAM is employed to evaluate whether the network accurately identifies the location of lung nodules. In the final test set, the proposed network achieves an accuracy of 89.13%, an F1-score of 84.85%, and a G-mean of 86.20%. These metrics represent improvements of 5.43%, 5.98%, and 4.09%, respectively, compared with the original base network. The experimental results align with those of previous studies on pulmonary nodule diagnosis networks, confirming the reliability and clinical applicability of the diagnostic outcomes.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Dual Attention Approach for DNN Based Automated Diabetic Retinopathy Grading","authors":"Tareque Bashar Ovi,&nbsp;Nomaiya Bashree,&nbsp;Hussain Nyeem,&nbsp;Md Abdul Wahed,&nbsp;Faiaz Hasanuzzaman Rhythm,&nbsp;Ayat Subah Alam","doi":"10.1002/ima.23175","DOIUrl":"https://doi.org/10.1002/ima.23175","url":null,"abstract":"<div>\u0000 \u0000 <p>Diabetic retinopathy (DR) poses a serious threat to vision, emphasising the need for early detection. Manual analysis of fundus images, though common, is error-prone and time-intensive. Existing automated diagnostic methods lack precision, particularly in the early stages of DR. This paper introduces the Soft Convolutional Block Attention Module-based Network (Soft-CBAMNet), a deep learning network designed for severity detection, which features Soft-CBAM attention to capture complex features from fundus images. The proposed network integrates both the convolutional block attention module (CBAM) and the soft-attention components, ensuring simultaneous processing of input features. Following this, attention maps undergo a max-pooling operation, and refined features are concatenated before passing through a dropout layer with a dropout rate of 50%. Experimental results on the APTOS dataset demonstrate the superior performance of Soft-CBAMNet, achieving an accuracy of 85.4% in multiclass DR grading. The proposed architecture has shown strong robustness and general feature learning capability, achieving a mean AUC of 0.81 on the IDRID dataset. Soft-CBAMNet's dynamic feature extraction capability across all classes is further justified by the inspection of intermediate feature maps. The model excels in identifying all stages of DR with increased precision, surpassing contemporary approaches. Soft-CBAMNet presents a significant advancement in DR diagnosis, offering improved accuracy and efficiency for timely intervention.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}