Intelligence-based medicine最新文献_第4页

V-NET-VGG16: Hybrid deep learning architecture for optimal segmentation and classification of multi-differentiated liver tumors

Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100210

Amine Ben Slama , Hanene Sahli , Yessine Amri , Salam Labidi

{"title":"V-NET-VGG16: Hybrid deep learning architecture for optimal segmentation and classification of multi-differentiated liver tumors","authors":"Amine Ben Slama , Hanene Sahli , Yessine Amri , Salam Labidi","doi":"10.1016/j.ibmed.2025.100210","DOIUrl":"10.1016/j.ibmed.2025.100210","url":null,"abstract":"<div><div>Liver cancer is a leading cause of cancer-related mortality worldwide, underscoring the importance of early and accurate diagnosis. This study aims to develop an automatic system for liver tumor detection and classification using Computed Tomography (CT) images, addressing the critical challenge of accurately segmenting liver tumors and classifying them as benign, malignant, or normal tissues.</div><div>The proposed method combines two advanced deep learning models: V-Net for tumor segmentation and VGG16 for classification. A liver CT dataset augmented with various transformations, was used to enhance the model's robustness. The data was split into training (70 %) and testing (30 %) sets. The V-Net model performs the segmentation, isolating the liver and tumor regions from the CT images, while VGG16 is used for the classification of tumor types based on the segmented data.</div><div>The results demonstrate the effectiveness of this hybrid approach. The V-Net model achieved a Dice score of 97.34 % for accurate tumor segmentation, while the VGG16 model attained a classification accuracy of 96.52 % in differentiating between benign, malignant, and normal cases. These results surpass several existing state-of-the-art approaches in liver tumors analysis, demonstrating the potential of the proposed method for reliable and efficient medical image processing.</div><div>In conclusion, the hybrid V-Net and VGG16 architecture offers a powerful tool for the segmentation and classification of liver tumors, providing a significant improvement over manual segmentation methods that are prone to human error. This approach could aid clinicians in early diagnosis and treatment planning. Future work will focus on expanding the dataset and applying the method to other types of cancer to assess the model's generalizability and effectiveness in broader clinical settings.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A robust deep learning algorithm for lung cancer detection from computed tomography images

Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100203

A.A. Abe , M. Nyathi , A.A. Okunade , W. Pilloy , B. Kgole , N. Nyakale

{"title":"A robust deep learning algorithm for lung cancer detection from computed tomography images","authors":"A.A. Abe , M. Nyathi , A.A. Okunade , W. Pilloy , B. Kgole , N. Nyakale","doi":"10.1016/j.ibmed.2025.100203","DOIUrl":"10.1016/j.ibmed.2025.100203","url":null,"abstract":"<div><div>Detecting lung cancer at its earliest stage offers the best possibility for a cure. Chest computed tomography (CT) scans are a valuable tool for early diagnosis. However, the initial stages of lung cancer may present patterns in the images that are not easily detectable by radiologist, potentially leading to misdiagnosis. Although automated approaches using deep learning (DL) algorithms have been proposed, it depends on a substantial amount of data to achieve diagnostic accuracy comparable to that of radiologists. To alleviate this challenge, this study proposes a DL algorithm that uses an ensemble of convolutional neural networks and trained on relatively small dataset (IQ_OTH/NCCD dataset) to automate lung cancer diagnosis from patient chest CT scans. The method achieved an accuracy of 98.17 %, a sensitivity of 98.21 %, and a specificity of 98.13 % when categorizing scans as either cancerous or non-cancerous. Similarly, it achieved an accuracy of 95.43 %, a sensitivity of 93.40 %, and a specificity of 97.09 % when classifying scans as normal or containing benign or malignant pulmonary nodules. These results demonstrate superior performance compared to previously proposed models, highlighting the effectiveness of DL algorithms for early lung cancer diagnosis and providing a valuable tool to assist radiologists in their assesments.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CTCovid19: Automatic Covid-19 model for Computed Tomography Scans Using Deep Learning

Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2024.100190

Carlos Antunes , João Rodrigues , António Cunha

引用次数: 0

Advancing drug discovery and development through GPT models: a review on challenges, innovations and future prospects

Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100233

Zhinya Kawa Othman , Mohamed Mustaf Ahmed , Olalekan John Okesanya , Adamu Muhammad Ibrahim , Shuaibu Saidu Musa , Bryar A. Hassan , Lanja Ibrahim Saeed , Don Eliseo Lucero-Prisno III

{"title":"Advancing drug discovery and development through GPT models: a review on challenges, innovations and future prospects","authors":"Zhinya Kawa Othman , Mohamed Mustaf Ahmed , Olalekan John Okesanya , Adamu Muhammad Ibrahim , Shuaibu Saidu Musa , Bryar A. Hassan , Lanja Ibrahim Saeed , Don Eliseo Lucero-Prisno III","doi":"10.1016/j.ibmed.2025.100233","DOIUrl":"10.1016/j.ibmed.2025.100233","url":null,"abstract":"<div><div>Advanced AI algorithms, notably generative pre-trained transformer (GPT) models, are revolutionizing healthcare and drug discovery and development by efficiently processing and interpreting large volumes of medical data. Specialized models, such as ProtGPT2 and BioGPT, extend their capabilities to protein engineering and biomedical text mining. Our study will contribute to ongoing discussions to revolutionize drug development, leading to a faster and more reliable validation of new therapeutic agents that are crucial for healthcare advancement and patient outcomes. GPT models, such as MTMol-GPT, are robust, generalizable, and provide important information for developing treatments for complicated disorders. SynerGPT utilizes a genetic algorithm to optimize prompts and select drug combinations for testing based on individual patient characteristics. Ligand generation for specific target proteins with potential drug activity is a significant stage in the drug design process, which enhances the quality of the synthesized compounds and augments the precision of capturing chemical structures and their activity correlations, highlighting the model's creativity and capability for innovative ligand design. Despite these advancements, there are still problems with the data volume, scalability, interpretability, and validation. Ethical considerations, robust methods, and omics data must be successfully integrated to develop AI for drug discovery and ensure successful deployment. In summary, these models significantly influence drug research and development, specifically in the earlier stages from initial target selection to post-marketing surveillance for medication safety monitoring.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing breast cancer detection in ultrasound images using a novel hybrid ensemble deep learning model

Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100222

Radwan Qasrawi , Omar Daraghmeh , Suliman Thwib , Ibrahem Qdaih , Ghada Issa , Stephanny Vicuna Polo , Haneen Owienah , Diala Abu Al-Halawa , Siham Atari

{"title":"Advancing breast cancer detection in ultrasound images using a novel hybrid ensemble deep learning model","authors":"Radwan Qasrawi , Omar Daraghmeh , Suliman Thwib , Ibrahem Qdaih , Ghada Issa , Stephanny Vicuna Polo , Haneen Owienah , Diala Abu Al-Halawa , Siham Atari","doi":"10.1016/j.ibmed.2025.100222","DOIUrl":"10.1016/j.ibmed.2025.100222","url":null,"abstract":"<div><div>Breast cancer remains a leading cause of mortality among women globally, emphasizing the critical need for prompt and accurate detection to improve patient outcomes. This study introduces an innovative hybrid model combining ultrasound image enhancement techniques with advanced machine learning for rapid and more accurate breast cancer prognosis. The proposed model integrates Contrast Limited Adaptive Histogram Equalization (CLAHE) for image quality improvement with an Ensemble Deep Random Vector Functional Link Neural Network (edRVFL) for classification. Utilizing a dataset of 4103 high-resolution ultrasound images from the Dunya Women's Cancer Center in Palestine, categorized into normal, benign, and malignant groups, the model was trained and evaluated using a 25-fold cross-validation approach. Results demonstrate higher performance of the hybrid model compared to traditional machine learning algorithms, achieving accuracies of 96 % for benign and 98 % for malignant cases after CLAHE enhancement. To further improve lesion detection and segmentation, a new method combining YOLOv5 object detection with the MedSAM foundation model was developed, achieving a Dice Similarity Coefficient of 0.988 after CLAHE enhancement. Validation in a clinical setting on 850 cases showed promising results, with 91.4 % ± 0.021 accuracy for benign and 84 % ± 0.024 for malignant predictions compared to histopathology. The model's high accuracy and interpretability, supported by Grad-CAM analysis, demonstrate its potential for integration into clinical practice. This study advances the application of machine learning in breast cancer detection from ultrasound images, presenting a valuable tool for enabling early detection and improving prognosis for breast cancer patients.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100222"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy based system for coronary artery disease prediction using subtractive clustering and risk factors data

Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100208

Abdeljalil El-Ibrahimi , Othmane Daanouni , Zakaria Alouani , Oussama El Gannour , Shawki Saleh , Bouchaib Cherradi , Omar Bouattane

{"title":"Fuzzy based system for coronary artery disease prediction using subtractive clustering and risk factors data","authors":"Abdeljalil El-Ibrahimi , Othmane Daanouni , Zakaria Alouani , Oussama El Gannour , Shawki Saleh , Bouchaib Cherradi , Omar Bouattane","doi":"10.1016/j.ibmed.2025.100208","DOIUrl":"10.1016/j.ibmed.2025.100208","url":null,"abstract":"<div><div>Over the past three decades, coronary artery disease (CAD) has been considered one of the most common fatal diseases worldwide. Consequently, early diagnosis and prediction are essential, as they can significantly reduce patient mortality and treatment costs. This study aims to design an automatic expert system using fuzzy logic theory to predict CAD. Thus, aiding physicians to identify diseases at an early stage and assess their severity. This system generates fuzzy rules automatically from training dataset through a subtractive clustering method and employs the Sugeno Fuzzy Inference Engine to produce an output indicating the patient's condition. Feature selection is performed using filter methods such as variance analysis, Mutual Information, and Pearson's Correlation Coefficient to identify the most relevant factors affecting heart disease. The implementation is conducted on publicly available UCI heart disease datasets, and the system's performance is evaluated based on accuracy, specificity, and sensitivity metrics. The findings indicate a classification accuracy of 99.61 %, achieving a sensitivity rate of 100 % and a specificity rate of 99.20 %. These findings highlight the system's potential as an effective diagnostic and early prevention tool, ultimately improving clinical outcomes in CAD treatment.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Breast cancer prediction using machine learning classification algorithms

Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2024.100193

Alan La Moglia , Khaled Mohamad Almustafa

{"title":"Breast cancer prediction using machine learning classification algorithms","authors":"Alan La Moglia , Khaled Mohamad Almustafa","doi":"10.1016/j.ibmed.2024.100193","DOIUrl":"10.1016/j.ibmed.2024.100193","url":null,"abstract":"<div><div>In bioinformatics, the integration of machine learning has revolutionized disease diagnosis. Machine learning algorithms remove human limitations, offering more accuracy in diagnosing diseases like cancer. Breast cancer, the second most diagnosed cancer in women, often relies on mammography, which is only 70 % accurate, leading to potential misdiagnosis. Biopsies, though more reliable, are subject to human error and conflicting specialist opinions, often requiring multiple biopsies. The shortage of pathologists further complicates accurate and timely diagnoses. Machine learning can reduce these errors, providing faster and more precise results. In this study, a breast cancer dataset with 11 features is analyzed using eight machine learning classifiers. Results showed that Logistic Regression achieved the highest testing accuracy of 91.67 % without feature selection. After applying feature selection, classifiers like LGBM improved, with a notable 90.74 % accuracy. This study highlights the importance of integrating machine learning into healthcare, not only for breast cancer but for other diseases like heart disease and diabetes. Continued exploration and application of machine learning in bioinformatics will enhance its accessibility and effectiveness for medical professionals worldwide, leading to improved patient outcomes.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid deep learning and active contour approach for enhanced breast lesion segmentation and classification in mammograms

Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100224

Abdala Nour, Boubakeur Boufama

{"title":"Hybrid deep learning and active contour approach for enhanced breast lesion segmentation and classification in mammograms","authors":"Abdala Nour, Boubakeur Boufama","doi":"10.1016/j.ibmed.2025.100224","DOIUrl":"10.1016/j.ibmed.2025.100224","url":null,"abstract":"<div><div>Accurate segmentation and classification of breast lesions in mammography images are crucial steps in effective breast cancer screening and diagnosis. This study presents a hybrid deep learning and active contour approach to automated mammogram analysis. The proposed methodology leverages the powerful feature extraction capabilities of deep convolutional neural networks and the precise boundary delineation of active contour models. A U-Net is trained on a large dataset of mammogram images to learn discriminative features and generate initial segmentation masks for breast lesions. Subsequently, an active contour refinement stage is employed to fine-tune the segmentation boundaries and enhance lesion delineation accuracy. This integration of active contour models (ACM) with deep learning techniques overcomes traditional image segmentation limitations. Morphological operations and energy minimization techniques are applied to the initial segmentation mask, resulting in highly accurate and refined lesion segmentation. This study investigates the synergistic integration of deep learning with Adaptive Contour Modeling for breast lesion segmentation. Our proposed U-Net_ACM model leverages the strengths of both approaches, demonstrating state-of-the-art performance and outperforming methods relying solely on deep learning or traditional image processing techniques. Evaluation on a test set reveals a 97.34 % accuracy, a Dice coefficient of 0.813, and an Intersection over Union of 0.891 for the U-Net_ACM model. These results surpass the performance of established pre-trained deep learning models such as VGG16, VGG19, and DeepLabV3, highlighting the benefits of the combined approach. This hybrid methodology offers a robust, automated solution for mammogram analysis, potentially improving breast cancer screening outcomes. The superior segmentation quality and overall performance demonstrated by the U-Net_ACM model suggest its potential for enhancing breast cancer screening and diagnosis in clinical settings.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100224"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Individual dynamic capabilities and artificial intelligence in health operations: Exploration of innovation diffusion

Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100239

Antonio Pesqueira , Maria José Sousa , Rúben Pereira

{"title":"Individual dynamic capabilities and artificial intelligence in health operations: Exploration of innovation diffusion","authors":"Antonio Pesqueira , Maria José Sousa , Rúben Pereira","doi":"10.1016/j.ibmed.2025.100239","DOIUrl":"10.1016/j.ibmed.2025.100239","url":null,"abstract":"<div><div>This research investigates the integration of individual dynamic capabilities (IDC), artificial intelligence (AI), and the Technology Acceptance Model (TAM) within health operations to evaluate their role in fostering innovation diffusion in healthcare. A convergent, multifaceted research approach encompassing quantitative and qualitative methodologies was employed, commencing with a systematic review of the extant literature. This was then complemented by the execution of focus group sessions involving 21 participants. The main objective of this sequential exploratory design was to synthesize existing research present an empirical validation of real-world case studies, and assess AI deployment challenges that influence operational efficiency and service quality in healthcare organizations. The findings underscore the importance of IDC in advancing healthcare practices by driving cross-functional adaptation, facilitating AI implementation, and ensuring smooth operational transformation in line with healthcare standards and best practices. The findings offer valuable insights for operational and executive-level decision-makers aiming to optimize health operations by integrating IDC and AI technologies, enhancing patient care, service quality, and innovative health solutions.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100239"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New cognitive computational strategy for optimizing brain tumour classification using magnetic resonance imaging Data

Intelligence-based medicine Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100215

R. Kishore Kanna , Ayodeji Olalekan Salau

{"title":"New cognitive computational strategy for optimizing brain tumour classification using magnetic resonance imaging Data","authors":"R. Kishore Kanna , Ayodeji Olalekan Salau","doi":"10.1016/j.ibmed.2025.100215","DOIUrl":"10.1016/j.ibmed.2025.100215","url":null,"abstract":"<div><div>The brain is one of the most important organs in the human body. It governs all actions whether one is aware of the action or not. Brain tumors occur when the system of cell division in the brain is disrupted. Brain tumors are frequently associated with severe malignancies worldwide. The uncontrolled accumulation and growth of these cells can lead to the formation of seizures or tumors with impaired brain function.</div><div>Magnetic resonance imaging (MRI) is a common technology used to detect brain lesions; however, manual analysis of MRI images by physicians is challenging due to uncertainty and time constraints. The aim of this paper is to introduce machine learning (ML) algorithms designed to increase the speed and cognitive statistical methods for brain tumor classification.</div><div>In this study, we proposed a novel penguin search-optimized quantum-enhanced support vector machine (PSO-QESVM) to categorize brain tumor using MRI data. We used a publicly accessible brain MR image dataset for brain tumor classification tasks which we obtained from an online source. A median filter (MF) was used as part of the pre-processing step to eliminate noise from the data. Using ResNet and VGG16, features were extracted from the pre-processed data.</div><div>The proposed method was implemented using Python 3.7+ software. A comparison was made between the suggested approach and other conventional algorithms. The results show the proposed method achieved a superior efficiency with regards to recall (98.9 %), accuracy (98.90 %), f1-score (98.5 %), and precision (98.7 %).</div><div>The study demonstrated the applicability of the suggested strategy for brain tumor classification. The suggested cognitive computational strategy achieved a promising performance. To reduce the size of the model and implement it on a real-time medical diagnosis framework, we intend to employ knowledge distillation techniques.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0