Computational Intelligence最新文献

{"title":"An Automated Histopathological Colorectal Cancer Multi-Class Classification System Based on Optimal Image Processing and Prominent Features","authors":"Tasnim Jahan Tonni,&nbsp;Shakil Rana,&nbsp;Kaniz Fatema,&nbsp;Asif Karim,&nbsp;Md. Awlad Hossen Rony,&nbsp;Md. Zahid Hasan,&nbsp;Md. Saddam Hossain Mukta,&nbsp;Sami Azam","doi":"10.1111/coin.70007","DOIUrl":"https://doi.org/10.1111/coin.70007","url":null,"abstract":"<div>\u0000 \u0000 <p>Colorectal cancer (CRC) is characterized by the uncontrollable growth of cancerous cells within the rectal mucosa. In contrast, colon polyps, precancerous growths, can develop into colon cancer, causing symptoms like rectal bleeding, abdominal pain, diarrhea, weight loss, and constipation. It is the leading cause of death worldwide, and this potentially fatal cancer severely afflicts the elderly. Furthermore, early diagnosis is crucial for effective treatment, as it is often more time-consuming and laborious for experts. This study improved the accuracy of CRC multi-class classification compared to previous research utilizing diverse datasets, such as NCT-CRC-HE-100 K (100,000 images) and CRC-VAL-HE-7 K (7,180 images). Initially, we utilized various image processing techniques on the NCT-CRC-HE-100 K dataset to improve image quality and noise-freeness, followed by multiple feature extraction and selection methods to identify prominent features from a large data hub and experimenting with different approaches to select the best classifiers for these critical features. The third ensemble model (XGB-LightGBM-RF) achieved an optimum accuracy of 99.63% with 40 prominent features using univariate feature selection methods. Moreover, the third ensemble model also achieved 99.73% accuracy from the CRC-VAL-HE-7 K dataset. After combining two datasets, the third ensemble model achieved 99.27% accuracy. In addition, we trained and tested our model with two different datasets. We used 80% data from NCT-CRC-HE-100 K and 20% data from CRC-VAL-HE-7 K, respectively, for training and testing purposes, while the third ensemble model obtained 98.43% accuracy in multi-class classification. The results show that this new framework, which was created using the third ensemble model, can help experts figure out what kinds of CRC diseases people are dealing with at the very beginning of an investigation.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861764","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":"Mining User Study Data to Judge the Merit of a Model for Supporting User-Specific Explanations of AI Systems","authors":"Owen Chambers,&nbsp;Robin Cohen,&nbsp;Maura R. Grossman,&nbsp;Liam Hebert,&nbsp;Elias Awad","doi":"10.1111/coin.70015","DOIUrl":"https://doi.org/10.1111/coin.70015","url":null,"abstract":"<p>In this paper, we present a model for supporting user-specific explanations of AI systems. We then discuss a user study that was conducted to gauge whether the decisions for adjusting output to users with certain characteristics was confirmed to be of value to participants. We focus on the merit of having explanations attuned to particular psychological profiles of users, and the value of having different options for the level of explanation that is offered (including allowing for no explanation, as one possibility). Following the description of the study, we present an approach for mining data from user participant responses in order to determine whether the model that was developed for varying the output to users was well-founded. While our results in this respect are preliminary, we explain how using varied machine learning methods is of value as a concrete step toward validation of specific approaches for AI explanation. We conclude with a discussion of related work and some ideas for new directions with the research, in the future.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861637","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":"Resan: A Residual Dual-Attention Network for Abnormal Cardiac Activity Detection","authors":"Xuhui Wang,&nbsp;Yuanyuan Zhu,&nbsp;Fei Wu,&nbsp;Long Gao,&nbsp;Datun Qi,&nbsp;Xiaoyuan Jing,&nbsp;Chong Luo","doi":"10.1111/coin.70005","DOIUrl":"https://doi.org/10.1111/coin.70005","url":null,"abstract":"<div>\u0000 \u0000 <p>Cardiovascular disease is one of the leading causes of death worldwide. Early and accurate detection of abnormal cardiac activity can be an effective way to prevent serious cardiovascular events. Electrocardiogram (ECG) and phonocardiogram (PCG) signals provide an objective evaluation of the heart's electrical and acoustic functions, enabling medical professionals to make an accurate diagnosis. Therefore, the cardiologists often use them to make a preliminary diagnosis of abnormal cardiac activity in clinical practice. For this reason, many diagnostic models have been proposed. However, these models fail to utilize the interaction information within and between the signals to aid the diagnosis of disease. To address this issue, we designed a residual dual-attention network (ResAN) for the detection of abnormal cardiac activity using synchronized ECG and PCG signals. First, ResAN uses a feature learning module with two parallel residual networks, for example, ECG-ResNet and PCG-ResNet to automatically learn the deep modal-specific features from the ECG and PCG sequences, respectively. Second, to fully utilize the available information of different modal signals, ResAN uses a dual-attention fusion module to capture the salient features of the integrated ECG and PCG features learned by the feature learning module, as well as the alternating features between them based on the attention mechanisms. Finally, these fused features are merged and fed to the classification module to detect abnormal cardiac activity. Our model achieves an accuracy of 96.1%, surpassing the performances of comparison models by 1.0% to 9.9% when using synchronized ECG and PCG signals. Furthermore, the ablation study confirmed the efficacy of the components in ResAN and also showed that ResAN performs better with synchronized ECG and PCG signals compared to using single-modal signals. Overall, ResAN provides a valid solution for the early detection of abnormal cardiac activity using ECG and PCG signals.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861639","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 ViT-Based Adaptive Recurrent Mobilenet With Attention Network for Video Compression and Bit-Rate Reduction Using Improved Heuristic Approach Under Versatile Video Coding","authors":"D. Padmapriya,&nbsp;Ameelia Roseline A","doi":"10.1111/coin.70014","DOIUrl":"https://doi.org/10.1111/coin.70014","url":null,"abstract":"<div>\u0000 \u0000 <p>Video compression received attention from the communities of video processing and deep learning. Modern learning-aided mechanisms use a hybrid coding approach to reduce redundancy in pixel space across time and space, improving motion compensation accuracy. The experiments in video compression have important improvements in past years. The Versatile Video Coding (VVC) is the primary enhancing standard of video compression which is also referred to as H. 226. The VVC codec is a block-assisted hybrid codec, making it highly capable and complex. Video coding effectively compresses data while reducing compression artifacts, enhancing the quality and functionality of AI video technologies. However, the traditional models suffer from the incorrect compression of the motion and ineffective compensation frameworks of the motion leading to compression faults with a minimal trade-off of the rate distortion. This work implements an automated and effective video compression task under VVC using a deep learning approach. Motion estimation is conducted using the Motion Vector (MV) encoder-decoder model to track movements in the video. Based on these MV, the reconstruction of the frame is carried out to compensate for the motions. The residual images are obtained by using Vision Transformer-based Adaptive Recurrent MobileNet with Attention Network (ViT-ARMAN). The parameters optimization of the ViT-ARMAN is done using the Opposition-based Golden Tortoise Beetle Optimizer (OGTBO). Entropy coding is used in the training phase of the developed work to find the bit rate of residual images. Extensive experiments were conducted to demonstrate the effectiveness of the developed deep learning-based method for video compression and bit rate reduction under VVC.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860566","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":"Homomorphisms and Embeddings of STRIPS Planning Models","authors":"Arnaud Lequen,&nbsp;Martin C. Cooper,&nbsp;Frédéric Maris","doi":"10.1111/coin.70013","DOIUrl":"https://doi.org/10.1111/coin.70013","url":null,"abstract":"&lt;p&gt;Determining whether two STRIPS planning instances are isomorphic is the simplest form of comparison between planning instances. It is also a particular case of the problem concerned with finding an isomorphism between a planning instance &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ P $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and a sub-instance of another instance &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;′&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {P}^{prime } $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. One application of such a mapping is to efficiently produce a compiled form containing all solutions to &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ P $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; from a compiled form containing all solutions to &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;′&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {P}^{prime } $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. We also introduce the notion of &lt;i&gt;embedding&lt;/i&gt; from an instance &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ P $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; to another instance &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;′&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {P}^{prime } $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, which allows us to deduce that &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;′&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {P}^{prime } $$&lt;/annotation&gt;\u0000 &lt;/s","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860567","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":"Beyond Words: ESC-Net Revolutionizes VQA by Elevating Visual Features and Defying Language Priors","authors":"Souvik Chowdhury,&nbsp;Badal Soni","doi":"10.1111/coin.70010","DOIUrl":"https://doi.org/10.1111/coin.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>Language prior is a pressing problem in the VQA domain where a model provides an answer favoring the most frequent related answer. There are some methods that are adopted to mitigate language prior issue, for example, ensemble approach, the balanced data approach, the modified evaluation strategy, and the modified training framework. In this article, we propose a VQA model, “Ensemble of Spatial and Channel Attention Network (ESC-Net),” to overcome the language bias problem by improving the visual features. In this work, we have used regional and global image features along with an ensemble of combined channel and spatial attention mechanisms to improve visual features. The model is a simpler and effective solution than existing methods to solve language bias. Extensive experiment show a remarkable performance improvement of 18% on the VQACP v2 dataset with a comparison to current state-of-the-art (SOTA) models.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762484","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":"Multi-View Self-Supervised Auxiliary Task for Few-Shot Remote Sensing Classification","authors":"Baodi Liu,&nbsp;Lei Xing,&nbsp;Xujian Qiao,&nbsp;Qian Liu","doi":"10.1111/coin.70009","DOIUrl":"https://doi.org/10.1111/coin.70009","url":null,"abstract":"<div>\u0000 \u0000 <p>In the past few years, the swift advancement of remote sensing technology has greatly promoted its widespread application in the agricultural field. For example, remote sensing technology is used to monitor the planting area and growth status of crops, classify crops, and detect agricultural disasters. In these applications, the accuracy of image classification is of great significance in improving the efficiency and sustainability of agricultural production. However, many of the existing studies primarily rely on contrastive self-supervised learning methods, which come with certain limitations such as complex data construction and a bias towards invariant features. To address these issues, additional techniques like knowledge distillation are often employed to optimize the learned features. In this article, we propose a novel approach to enhance feature acquisition specific to remote sensing images by introducing a classification-based self-supervised auxiliary task. This auxiliary task involves performing image transformation self-supervised learning tasks directly on the remote sensing images, thereby improving the overall capacity for feature representation. In this work, we design a texture fading reinforcement auxiliary task to reinforce texture features and color features that are useful for distinguishing similar classes of remote sensing. Different auxiliary tasks are fused to form a multi-view self-supervised auxiliary task and integrated with the main task to optimize the model training in an end-to-end manner. The experimental results on several popular few-shot remote sensing image datasets validate the effectiveness of the proposed method. The performance better than many advanced algorithms is achieved with a more concise structure.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142724263","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":"AgriFusion: A Low-Carbon Sustainable Computing Approach for Precision Agriculture Through Probabilistic Ensemble Crop Recommendation","authors":"T. R. Mahesh,&nbsp;Arastu Thakur,&nbsp;A. K. Velmurugan,&nbsp;Surbhi Bhatia Khan,&nbsp;Thippa Reddy Gadekallu,&nbsp;Saeed Alzahrani,&nbsp;Mohammed Alojail","doi":"10.1111/coin.70006","DOIUrl":"https://doi.org/10.1111/coin.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>Optimizing crop production is essential for sustainable agriculture and food security. This study presents the AgriFusion Model, an advanced ensemble-based machine learning framework designed to enhance precision agriculture by offering highly accurate and low-carbon crop recommendations. By integrating Random Forest, Gradient Boosting, and LightGBM, the model combines their strengths to boost predictive accuracy, robustness, and energy efficiency. Trained on a comprehensive dataset of 2200 instances covering key parameters like nitrogen, phosphorus, potassium, temperature, humidity, pH, rainfall, and crop type, the model underwent rigorous preprocessing for data integrity. The RandomizedSearchCV method was employed to do hyperparameter tuning, namely improving the number of trees in the Random Forest algorithm and the learning rates in the Gradient Boosting algorithm. This ensemble approach achieves a remarkable accuracy rate of 99.48%, optimizes computer resources, lowers carbon footprint, and responds efficiently to a variety of agricultural situations. The model's performance is confirmed using metrics including cross-validation, accuracy, precision, recall, and F1 score. This demonstrates how the model might improve agricultural decision-making, make the most use of available resources, and promote ecologically responsible farming practices.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692074","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":"SDKT: Similar Domain Knowledge Transfer for Multivariate Time Series Classification Tasks","authors":"Jiaye Wen,&nbsp;Wenan Zhou","doi":"10.1111/coin.70008","DOIUrl":"https://doi.org/10.1111/coin.70008","url":null,"abstract":"<div>\u0000 \u0000 <p>Multivariate time series data classification has a wide range of applications in reality. With rapid development of deep learning, convolutional networks are widely used in this task and have achieved the current best performance. However, due to high difficulty and cost of collecting this type of data, labeled data is still scarce. In some tasks, the model shows overfitting, resulting in relatively poor classification performance. In order to improve the classification performance under such situation, we proposed a novel classification method based on transfer learning—similar domain knowledge transfer (call SDKT for short). Firstly, we designed a multivariate time series domain distance calculation method (call MTSDDC for short), which helped selecting the source domain that is most similar to target domain; Secondly, we used ResNet as a pre-trained classifier, transferred the parameters of the similar domain network to the target domain network and continue to fine-tune the parameters. To verify our method, we conducted experiments on several public datasets. Our study has also shown that the transfer effect from the source domain to the target domain is highly negatively correlated with the distance between them, with an average Pearson coefficient of −0.78. For the transfer of most similar source domain, compared to the ResNet model without transfer and the current best model, the average accuracy improvements on the datasets we used are 4.01% and 1.46% respectively.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685335","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":"An Efficient and Robust 3D Medical Image Classification Approach Based on 3D CNN, Time-Distributed 2D CNN-BLSTM Models, and mRMR Feature Selection","authors":"Enver Akbacak,&nbsp;Nedim Muzoğlu","doi":"10.1111/coin.70000","DOIUrl":"https://doi.org/10.1111/coin.70000","url":null,"abstract":"<div>\u0000 \u0000 <p>The advent of 3D medical imaging has been a turning point in the diagnosis of various diseases, as voxel information from adjacent slices helps radiologists better understand complex anatomical relationships. However, the interpretation of medical images by radiologists with different levels of expertise can vary and is also time-consuming. In the last decades, artificial intelligence-based computer-aided systems have provided fast and more reliable diagnostic insights with great potential for various clinical purposes. This paper proposes a significant deep learning based 3D medical image diagnosis method. The method classifies MedMNIST3D, which consists of six 3D biomedical datasets obtained from CT, MRA, and electron microscopy modalities. The proposed method concatenates 3D image features extracted from three independent networks, a 3D CNN, and two time-distributed ResNet BLSTM structures. The ultimate discriminative features are selected via the minimum redundancy maximum relevance (mRMR) feature selection method. Those features are then classified by a neural network model. Experiments adhere to the rules of the official splits and evaluation metrics of the MedMNIST3D datasets. The results reveal that the proposed approach outperforms similar studies in terms of accuracy and AUC.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525058","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}