Network-Computation in Neural Systems最新文献

Hybrid ladybug Hawk optimization-enabled deep learning for multimodal Parkinson's disease classification using voice signals and hand-drawn images. 混合瓢虫鹰优化支持深度学习的多模态帕金森病分类使用语音信号和手绘图像。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-08-01 Epub Date: 2025-03-04 DOI: 10.1080/0954898X.2025.2457955

Shanthini Shanmugam, Chandrasekar Arumugam

{"title":"Hybrid ladybug Hawk optimization-enabled deep learning for multimodal Parkinson's disease classification using voice signals and hand-drawn images.","authors":"Shanthini Shanmugam, Chandrasekar Arumugam","doi":"10.1080/0954898X.2025.2457955","DOIUrl":"10.1080/0954898X.2025.2457955","url":null,"abstract":"PD is a progressive neurodegenerative disorder that leads to gradual motor impairments. Early detection is critical for slowing the disease's progression and providing patients access to timely therapies. However, accurately detecting PD in its early stages remains challenging. This study aims to develop an optimized deep learning model for PD classification using voice signals and hand-drawn spiral images, leveraging a ZFNet-LHO-DRN. The proposed model first preprocesses the input voice signal using a Gaussian filter to remove noise. Features are then extracted from the preprocessed signal and passed to ZFNet to generate output-1. For the hand-drawn spiral image, preprocessing is performed with a bilateral filter, followed by image augmentation. Here also, the features are extracted and forwarded to DRN to form output-2. Both classifiers are trained using the LHO algorithm. Finally, from the output-1 and output-2, the best one is selected based on the majority voting. The ZFNet-LHO-DRN model demonstrated excellent performance by achieving a premium accuracy of 89.8%, a NPV of 89.7%, a PPV of 89.7%, a TNR of 89.3%, and a TPR of 90.1%. The model's high accuracy and performance indicate its potential as a valuable tool for assisting in the early diagnosis of PD.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"651-693"},"PeriodicalIF":1.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging the internet of things and optimized deep residual networks for improved foliar disease detection in apple orchards. 利用物联网和优化的深度残留网络来改进苹果园的叶面疾病检测。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-08-01 Epub Date: 2025-03-24 DOI: 10.1080/0954898X.2025.2472626

Sameera Kuppam, Swarnalatha Purushotham

{"title":"Leveraging the internet of things and optimized deep residual networks for improved foliar disease detection in apple orchards.","authors":"Sameera Kuppam, Swarnalatha Purushotham","doi":"10.1080/0954898X.2025.2472626","DOIUrl":"10.1080/0954898X.2025.2472626","url":null,"abstract":"Plant diseases significantly threaten food security by reducing the quantity and quality of agricultural products. This paper presents a deep learning approach for classifying foliar diseases in apple plants using the Tunicate Swarm Sine Cosine Algorithm-based Deep Residual Network (TSSCA-based DRN). Cluster heads in simulated Internet of Things (IoT) networks are selected by Fractional Lion Optimization (FLION), and images are pre-processed with a Gaussian filter and segmented using the DeepJoint model. The TSSCA, combining the Tunicate Swarm Algorithm (TSA) and Sine Cosine Algorithm (SCA), enhances the classifier's effectiveness. Moreover, Plant Pathology 2020 - FGVC7 dataset is used in this work. This dataset is designed for the classification of foliar diseases in apple trees. The TSSCA-based DRN outperforms other methods, achieving 97% accuracy, 94.666% specificity, 96.888% sensitivity, and 0.0442J maximal energy, with significant improvements over existing approaches. Additionally, the proposed model demonstrates superior accuracy, outperforming other methods by 8.97%, 6.58%, 2.07%, 1.71%, 1.14%, 1.07%, 0.93%, and 0.64% over Multidimensional Feature Compensation Residual neural network (MDFC - ResNet), Convolutional Neural Network (CNN), Multi-Context Fusion Network (MCFN), Advanced Segmented Dimension Extraction (ASDE), and DRN, fuzzy deep convolutional neural network (FCDCNN), ResNet9-SE, Capsule Neural Network (CapsNet), IoT-based scrutinizing model, and Multi-Model Fusion Network (MMF-Net).","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"726-762"},"PeriodicalIF":1.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid deep learning based stroke detection using CT images with routing in an IoT environment. 基于混合深度学习的脑卒中检测，在物联网环境中使用CT图像和路由。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-08-01 Epub Date: 2025-02-01 DOI: 10.1080/0954898X.2025.2452280

Anchana Balakrishnannair Sreekumari, Arul Teen Yesudasan Paulsy

{"title":"Hybrid deep learning based stroke detection using CT images with routing in an IoT environment.","authors":"Anchana Balakrishnannair Sreekumari, Arul Teen Yesudasan Paulsy","doi":"10.1080/0954898X.2025.2452280","DOIUrl":"10.1080/0954898X.2025.2452280","url":null,"abstract":"Stroke remains a leading global health concern and early diagnosis and accurate identification of stroke lesions are essential for improving treatment outcomes and reducing long-term disabilities. Computed Tomography (CT) imaging is widely used in clinical settings for diagnosing stroke, assessing lesion size, and determining the severity. However, the accurate segmentation and early detection of stroke lesions in CT images remain challenging. Thus, a Jaccard_Residual SqueezeNet is proposed for predicting stroke from CT images with the integration of the Internet of Things (IoT). The Jaccard_Residual SqueezeNet is the integration of the Jaccard index in Residual SqueezeNet. Firstly, the brain CT image is routed to the Base Station (BS) using the Fractional Jellyfish Search Pelican Optimization Algorithm (FJSPOA) and preprocessing is accomplished by median filter. Then, the skull segmentation is accomplished by ENet and then feature extraction is done. Lastly, Stroke is detected using the Jaccard_Residual SqueezeNet. The values of throughput, energy, distance, trust, and delay determined in terms of routing are 72.172 Mbps, 0.580J, 22.243 m, 0.915, and 0.083S. Also, the accuracy, sensitivity, precision, and F1-score for stroke detection are 0.902, 0.896, 0.916, and 0.906. These findings suggest that Jaccard_Residual SqueezeNet offers a robust and efficient platform for stroke detection.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"536-575"},"PeriodicalIF":1.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved bounding box segmentation technique for crowd anomaly detection with optimal trained convolutional neural network. 基于最优训练卷积神经网络的人群异常检测改进边界盒分割技术。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-08-01 Epub Date: 2025-03-12 DOI: 10.1080/0954898X.2025.2475070

Rohini P S, Sowmy I

{"title":"Improved bounding box segmentation technique for crowd anomaly detection with optimal trained convolutional neural network.","authors":"Rohini P S, Sowmy I","doi":"10.1080/0954898X.2025.2475070","DOIUrl":"10.1080/0954898X.2025.2475070","url":null,"abstract":"A crucial role in many security and surveillance applications is crowd anomaly detection, where seeing unusual activity helps avert possible threats or interruptions. For precise anomaly identification, current models might not successfully incorporate spatial and temporal features. To overcome these drawbacks, a novel Crowd Anomaly Detection based on Opposition Behavior Learning updated Chimp Optimization Algorithm (CAD-OBLChoA) is proposed in this research to enhance the detection of abnormal crowd behaviours in dynamic environments. In this research, bilateral filtering is used for smoothening the image and reducing noise for preprocessing phase. For object detection, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-based bounding box approach is used. Then, features like Colour features, Shape features, and Improved Texture features are extracted. Finally, the anomalies get detected based on the trained extracted feature set in the system. For this, an optimized CNN is used, where training is done by the OBLChoA scheme via tuning the optimal weights. The proposed CAD-OBLChoA scheme achieved a higher specificity of about 0.924 and 0.931 in the 90% training data for datasets 1 and 2. This approach could significantly improve crowd monitoring and security, enabling faster identification of potential threats or emergencies.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"763-816"},"PeriodicalIF":1.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel skin cancer detection architecture using tangent rat swarm optimization algorithm enabled DenseNet. 一种基于切线鼠群优化算法的新型皮肤癌检测架构实现了DenseNet。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-08-01 Epub Date: 2025-02-28 DOI: 10.1080/0954898X.2025.2452274

Balashanmuga Vadivu P, Om Prakash Pg, Aravind Karrothu, Sriramakrishnan Gv

{"title":"A novel skin cancer detection architecture using tangent rat swarm optimization algorithm enabled DenseNet.","authors":"Balashanmuga Vadivu P, Om Prakash Pg, Aravind Karrothu, Sriramakrishnan Gv","doi":"10.1080/0954898X.2025.2452274","DOIUrl":"10.1080/0954898X.2025.2452274","url":null,"abstract":"This paper proposes a Tangent Rat Swarm Optimization (TRSO)-DenseNet for the detection of skin cancer to reduce the severity rate of cancer. Initially, the input image is pre-processed by employing a linear smoothing filter. The pre-processed image is transferred to skin lesion segmentation, where Mask-RCNN is utilized for segmenting the skin lesion. Then, image augmentation is performed using techniques such as vertical shifting, horizontal shifting, random rotation, brightness adjustment, blurring, and cropping. The augmented image is then fed into the feature extraction phase to identify statistical features, Haralick texture features, Convolutional Neural Network (CNN) features, Local Ternary Pattern (LTP), Histogram of Oriented Gradients (HOG), and Local Vector Pattern (LVP). Finally, the extracted features are fed into the skin cancer detection phase, where DenseNet is used to detect skin cancer. Here, DenseNet is structurally optimized by TRSO, which has the combination of the Tangent Search Algorithm (TSA) and Rat Swarm Optimizer (RSO). The TRSO-DenseNet model is implemented using MATLAB tool and analayzsed using the Society for Imaging Informatics in Medicine-International Skin Imaging Collaboration's (SIIM-ISIC) Melanoma Classification dataset. The Proposed model for skin cancer detection attained superior performance with an accuracy of 94.63%, TPR of 91.51%, and TNR of 92.46%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"505-535"},"PeriodicalIF":1.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ZF-QDCNN: ZFNet and quantum dilated convolutional neural network based Alzheimer's disease detection using MRI images. ZF-QDCNN：基于ZFNet和量子扩张卷积神经网络的阿尔茨海默病MRI图像检测。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-08-01 Epub Date: 2025-02-11 DOI: 10.1080/0954898X.2025.2452288

Sharda Yashwant Salunkhe, Mahesh S Chavan

{"title":"ZF-QDCNN: ZFNet and quantum dilated convolutional neural network based Alzheimer's disease detection using MRI images.","authors":"Sharda Yashwant Salunkhe, Mahesh S Chavan","doi":"10.1080/0954898X.2025.2452288","DOIUrl":"10.1080/0954898X.2025.2452288","url":null,"abstract":"Alzheimer's disease (AD) is a severe neurological disorder that leads to irreversible memory loss. In the previous research, the early-stage Alzheimer's often presents with subtle memory issues that are difficult to differentiate from normal age-related changes. This research designed a novel detection model called the Zeiler and Fergus Quantum Dilated Convolutional Neural Network (ZF-QDCNN) for AD detection using Magnetic Resonance Imaging (MRI). Initially, the input MRI images are taken from a specific dataset, which is pre-processed using a Gaussian filter. Then, the brain area segmentation is performed by utilizing the Channel-wise Feature Pyramid Network for Medicine (CFPNet-M). After segmentation, relevant features are extracted, and the classification of AD is performed using the ZF-QDCNN, which is the integration of the Zeiler and Fergus Network (ZFNet) with the Quantum Dilated Convolutional Neural Network (QDCNN). Moreover, the ZF-QDCNN model demonstrated promising performance, achieving an accuracy of 91.7%, a sensitivity of 90.7%, a specificity of 92.7%, and a f-measure of 91.8% in detecting AD. Additionally, the proposed ZF-QDCNN model effectively identifies and classifies Alzheimer's disease in MRI images, highlighting its potential as a valuable tool for early diagnosis and management of the condition.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"576-620"},"PeriodicalIF":1.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-objective function for deep learning-based automatic energy efficiency power allocation in multicarrier noma system using hybrid heuristic improvement. 基于混合启发式改进的基于深度学习的多载波noma系统能效自动分配多目标函数。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-08-01 Epub Date: 2025-03-13 DOI: 10.1080/0954898X.2025.2461046

Chiranjeevi Thokala, Pradnya H Ghare

{"title":"A multi-objective function for deep learning-based automatic energy efficiency power allocation in multicarrier noma system using hybrid heuristic improvement.","authors":"Chiranjeevi Thokala, Pradnya H Ghare","doi":"10.1080/0954898X.2025.2461046","DOIUrl":"10.1080/0954898X.2025.2461046","url":null,"abstract":"Non-Orthogonal Multiple Access (NOMA) is the successive multiple-access methodologies for modern communication devices. Energy Efficiency (EE) is suggested in the NOMA system. In dynamic network conditions, the consideration of NOMA shows high computational complexity that minimizes the EE to degrade the system performance. This research suggested EE for the Multi-Carrier NOMA (MC-NOMA) models by optimization algorithm. The main scope of this research tends to improve the EE by Hybrid of Sewing Training and Lemur Optimization for optimizing the system parameters. The improvement made in this developed HSTLO algorithm can provide significant impact on MC-NOMA system, which it renders better user capacity while effectively optimizing the system parameters. Moreover, the Dilated Dense Recurrent Neural Network (DDRNN) model is developed. Employing the improvement in the deep learning model for the MC-NOMA system could effectively manage and enhance the system performance. Considering the DDRNN model can leverage to provide better generalization outcomes in different network scenarios that ensures to provide fast and reliable solutions compared to existing methods. Addressing the energy consumption problems in this research study will be analysed to show the advancement in MC-NOMA system that help to enhance the system performance.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"694-725"},"PeriodicalIF":1.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143617226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

JLeNeT: Jaccard LeNet for Parkinson's disease detection and severity level classification using voice signal in IoT environment. JLeNeT：在物联网环境中使用语音信号进行帕金森病检测和严重程度分类的Jaccard LeNet。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-08-01 Epub Date: 2025-03-12 DOI: 10.1080/0954898X.2025.2453032

Sundaresan Pragadeeswaran, Subramanian Kannimuthu

{"title":"JLeNeT: Jaccard LeNet for Parkinson's disease detection and severity level classification using voice signal in IoT environment.","authors":"Sundaresan Pragadeeswaran, Subramanian Kannimuthu","doi":"10.1080/0954898X.2025.2453032","DOIUrl":"10.1080/0954898X.2025.2453032","url":null,"abstract":"The neurodegenerative disorder called Parkinson's disease (PD) is one of the most common diseases now a day. In this research, PD is detected and severity classification is done using the proposed Jaccard LeNet (JLeNet) with the help of voice signal in the IoT environment. Here, the IoT simulation is done. Initially, from which voice signal is collected and the routing process is done by the proposed Chimp Wild Geese Algorithm (ChWGA). This ChWGA is the combination of the Wild Geese Algorithm (WGA) and Chimp Optimization Algorithm (ChOA). Finally, at Base Station (BS), PD is detected and classified. The input voice signal is fed for pre-processing conducted by an adaptive Kalman filter. Following this, feature extraction and feature selection are conducted, where Harmonic mean similarity helps in feature selection. Here, PD is detected using JLeNet, which is the hybridization of LeNet with the Jaccard similarity measure. In this work, routing metrics of energy and delay are superior and recorded with the values of 0.309 J and 0.434 ms for the ChWGA. Moreover, the proposed method attains an Accuracy of 0.910, True Positive Rate (TPR) of 0.903, and True Negative Rate (TNR) of 0.918.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"621-650"},"PeriodicalIF":1.6,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143607157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust text-dependent speaker verification system using gender aware Siamese-Triplet Deep Neural Network. 基于性别感知暹罗-三重深度神经网络的鲁棒文本依赖说话人验证系统。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-05-01 Epub Date: 2024-12-29 DOI: 10.1080/0954898X.2024.2438128

Sanghamitra V Arora

{"title":"Robust text-dependent speaker verification system using gender aware Siamese-Triplet Deep Neural Network.","authors":"Sanghamitra V Arora","doi":"10.1080/0954898X.2024.2438128","DOIUrl":"10.1080/0954898X.2024.2438128","url":null,"abstract":"Speaker verification in text-dependent scenarios is critical for high-security applications but faces challenges such as voice quality variations, linguistic diversity, and gender-related pitch differences, which affect authentication accuracy. This paper introduces a Gender-Aware Siamese-Triplet Network-Deep Neural Network (ST-DNN) architecture to address these challenges. The Gender-Aware Network utilizes Convolutional 2D layers with ReLU activation for initial feature extraction, followed by multi-fusion dense skip connections and batch normalization to integrate features across different depths, enhancing discrimination between male and female speakers. A bottleneck layer compresses feature maps to capture gender-related characteristics effectively. For enhanced speaker verification, separate male and female ST-DNN models are used, each incorporating Individual, Siamese, and Triplet Networks. The Individual Network extracts unique utterance characteristics, the Siamese Network compares speech sample pairs for speaker identity, and the Triplet Network ensures closely grouped embeddings of samples from the same speaker, facilitating precise verification. Experimental results on RSR2015 and RedDots Challenge 2016 datasets demonstrate significant improvements, with reductions in Equal Error Rate (EER) ranging from 32.31% to 54.55% for males and 33.73% to 38.98% for females, and reductions in MinDCF from 53.47% to 86.36% and 39.46% to 71.19%, respectively, validating the efficacy of the ST-DNN in real-world applications.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"233-272"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized recommendation system to handle skin cancer at early stage based on hybrid model. 基于混合模型的皮肤癌早期治疗个性化推荐系统。

IF 1.6 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2026-05-01 Epub Date: 2025-01-15 DOI: 10.1080/0954898X.2024.2449173

Siva Prasad Reddy K V, Meera Selvakumar

{"title":"Personalized recommendation system to handle skin cancer at early stage based on hybrid model.","authors":"Siva Prasad Reddy K V, Meera Selvakumar","doi":"10.1080/0954898X.2024.2449173","DOIUrl":"10.1080/0954898X.2024.2449173","url":null,"abstract":"Skin cancer is one of the most prevalent and harmful forms of cancer, with early detection being crucial for successful treatment outcomes. However, current skin cancer detection methods often suffer from limitations such as reliance on manual inspection by clinicians, inconsistency in diagnostic accuracy, and a lack of personalized recommendations based on patient-specific data. In our work, we presented a Personalized Recommendation System to handle Skin Cancer at an early stage based on Hybrid Model (PRSSCHM). Preprocessing, improved deep joint segmentation, feature extraction, and classification are the major steps to identify the stages of skin cancer. The input image is first preprocessed using the Gaussian filtering method. Improved deep joint segmentation is employed to segment the preprocessed image. A set of features including Median Binary Pattern (MBP), Gray Level Co-occurrence Matrix (GLCM), and Improved Local Direction Texture Pattern (ILDTP) are extracted in the next step. Finally, the hybrid classification includes Improved Bi-directional Long Short-Term Memory (Bi-LSTM) and Deep Belief Network (DBN) used for the classification process, where the training will be carried out by the Integrated Bald Eagle and Average and Subtraction Optimizer (IBEASO) algorithm via optimizing the weights of the models.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"444-483"},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0