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Medical Imaging Based Disease Diagnosis Using AI

Submission Deadline: 31 December 2024 (closed) View: 2328

Guest Editors

Dr. Azhar Imran, Air University, Pakistan
Prof. Jianqiang Li, Beijing University of Technology, China
Dr. Khursheed Aurangzeb, King Saud University, Saudi Arabia

Summary

Medical Imaging Based Disease Diagnosis using AI is about how AI technologies have found their way into medical imaging methods and are utilized for disease diagnosis. Human experts may find it challenging to analyze extensive data from traditional medical imaging techniques such as X-rays, CT scans, and MRIs. Machine learning algorithms closely linked to AI help interpret complex datasets by detecting disease patterns, anomalies, and subtle features. The above approach will tremendously improve the diagnosis process by making it faster and more accurate. AI algorithms can quickly analyze a large number of medical images, hence helping radiologists and clinicians make early disease diagnoses, predict patient outcomes, and customize treatment plans. In addition to making the diagnostic process shorter, adopting AI in medical imaging also improves strategies for treating patients with high accuracy.


Analyzing medical images is integral to many AI techniques, such as deep learning and computer vision. An AI system that employs deep learning algorithms for analyzing these images can learn autonomously from large data sets, and in so doing, its accuracy in detecting patterns related to various diseases continues to increase. This adaptability makes them effective tools for identifying anomalies in medical images. However, other challenges yet to be addressed include extensive labeled datasets, interpretability of AI-generated diagnoses, and regulatory considerations necessary for the responsible use of AI in medical imaging. As the field progresses through research and development endeavors, the interaction between AI capabilities and medical knowledge could lead to crucial avenues for disease diagnosis that would benefit patients.


Keywords

Medical Imaging
Disease Diagnosis
Artificial Intelligence (AI)
Early Disease Detection
Medical Image Analysis
Healthcare Informatics
Machine Learning Algorithms
Deep Learning
Pattern Recognition
Precision Medicine

Published Papers


  • Open Access

    ARTICLE

    An Attention-Based CNN Framework for Alzheimer’s Disease Staging with Multi-Technique XAI Visualization

    Mustafa Lateef Fadhil Jumaili, Emrullah Sonuç
    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2947-2969, 2025, DOI:10.32604/cmc.2025.062719
    (This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
    Abstract Alzheimer’s disease (AD) is a significant challenge in modern healthcare, with early detection and accurate staging remaining critical priorities for effective intervention. While Deep Learning (DL) approaches have shown promise in AD diagnosis, existing methods often struggle with the issues of precision, interpretability, and class imbalance. This study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence (XAI) techniques, in particular attention mechanisms, Gradient-Weighted Class Activation Mapping (Grad-CAM), and Local Interpretable Model-Agnostic Explanations (LIME), to improve both model interpretability and feature selection. The study evaluates four different DL architectures (ResMLP, VGG16, Xception, More >

  • Open Access

    ARTICLE

    Leveraging Edge Optimize Vision Transformer for Monkeypox Lesion Diagnosis on Mobile Devices

    Poonam Sharma, Bhisham Sharma, Dhirendra Prasad Yadav, Surbhi Bhatia Khan, Ahlam Almusharraf
    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3227-3245, 2025, DOI:10.32604/cmc.2025.062376
    (This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
    Abstract Rapid and precise diagnostic tools for Monkeypox (Mpox) lesions are crucial for effective treatment because their symptoms are similar to those of other pox-related illnesses, like smallpox and chickenpox. The morphological similarities between smallpox, chickenpox, and monkeypox, particularly in how they appear as rashes and skin lesions, which can sometimes make diagnosis challenging. Chickenpox lesions appear in many simultaneous phases and are more diffuse, often beginning on the trunk. In contrast, monkeypox lesions emerge progressively and are typically centralized on the face, palms, and soles. To provide accessible diagnostics, this study introduces a novel method… More >

  • Open Access

    ARTICLE

    A Global-Local Parallel Dual-Branch Deep Learning Model with Attention-Enhanced Feature Fusion for Brain Tumor MRI Classification

    Zhiyong Li, Xinlian Zhou
    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 739-760, 2025, DOI:10.32604/cmc.2025.059807
    (This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
    Abstract Brain tumor classification is crucial for personalized treatment planning. Although deep learning-based Artificial Intelligence (AI) models can automatically analyze tumor images, fine details of small tumor regions may be overlooked during global feature extraction. Therefore, we propose a brain tumor Magnetic Resonance Imaging (MRI) classification model based on a global-local parallel dual-branch structure. The global branch employs ResNet50 with a Multi-Head Self-Attention (MHSA) to capture global contextual information from whole brain images, while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor regions. The features from both branches are processed through More >

  • Open Access

    ARTICLE

    Multi-Scale Feature Fusion Network Model for Wireless Capsule Endoscopic Intestinal Lesion Detection

    Shiren Ye, Qi Meng, Shuo Zhang, Hui Wang
    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2415-2429, 2025, DOI:10.32604/cmc.2024.058250
    (This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
    Abstract WCE (Wireless Capsule Endoscopy) is a new technology that combines computer vision and medicine, allowing doctors to visualize the conditions inside the intestines, achieving good diagnostic results. However, due to the complex intestinal environment and limited pixel resolution of WCE videos, lesions are not easily detectable, and it takes an experienced doctor 1–2 h to analyze a complete WCE video. The use of computer-aided diagnostic methods, assisting or even replacing manual WCE diagnosis, has significant application value. In response to the issue of intestinal lesion detection in WCE videos, this paper proposes a multi-scale feature… More >

  • Open Access

    ARTICLE

    Attention Eraser and Quantitative Measures for Automated Bone Age Assessment

    Liuqiang Shu, Lei Yu
    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 627-644, 2025, DOI:10.32604/cmc.2024.056077
    (This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
    Abstract Bone age assessment (BAA) aims to determine whether a child’s growth and development are normal concerning their chronological age. To predict bone age more accurately based on radiographs, and for the left-hand X-ray images of different races model can have better adaptability, we propose a neural network in parallel with the quantitative features from the left-hand bone measurements for BAA. In this study, a lightweight feature extractor (LFE) is designed to obtain the feature maps from radiographs, and a module called attention eraser module (AEM) is proposed to capture the fine-grained features. Meanwhile, the dimensional… More >

  • Open Access

    ARTICLE

    ResMHA-Net: Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

    Novsheena Rasool, Javaid Iqbal Bhat, Najib Ben Aoun, Abdullah Alharthi, Niyaz Ahmad Wani, Vikram Chopra, Muhammad Shahid Anwar
    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 885-909, 2024, DOI:10.32604/cmc.2024.055900
    (This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
    Abstract Gliomas are aggressive brain tumors known for their heterogeneity, unclear borders, and diverse locations on Magnetic Resonance Imaging (MRI) scans. These factors present significant challenges for MRI-based segmentation, a crucial step for effective treatment planning and monitoring of glioma progression. This study proposes a novel deep learning framework, ResNet Multi-Head Attention U-Net (ResMHA-Net), to address these challenges and enhance glioma segmentation accuracy. ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms. This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture… More >

  • Open Access

    ARTICLE

    Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

    Mahesh Thyluru Ramakrishna, Kuppusamy Pothanaicker, Padma Selvaraj, Surbhi Bhatia Khan, Vinoth Kumar Venkatesan, Saeed Alzahrani, Mohammad Alojail
    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 867-883, 2024, DOI:10.32604/cmc.2024.053563
    (This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
    Abstract Brain tumor is a global issue due to which several people suffer, and its early diagnosis can help in the treatment in a more efficient manner. Identifying different types of brain tumors, including gliomas, meningiomas, pituitary tumors, as well as confirming the absence of tumors, poses a significant challenge using MRI images. Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification. These methods often rely on manual feature extraction and basic convolutional neural networks (CNNs). The limitations include inadequate accuracy, poor generalization of new data, and limited ability… More >

  • Open Access

    ARTICLE

    EfficientNetB1 Deep Learning Model for Microscopic Lung Cancer Lesion Detection and Classification Using Histopathological Images

    Rabia Javed, Tanzila Saba, Tahani Jaser Alahmadi, Sarah Al-Otaibi, Bayan AlGhofaily, Amjad Rehman
    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 809-825, 2024, DOI:10.32604/cmc.2024.052755
    (This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
    Abstract Cancer poses a significant threat due to its aggressive nature, potential for widespread metastasis, and inherent heterogeneity, which often leads to resistance to chemotherapy. Lung cancer ranks among the most prevalent forms of cancer worldwide, affecting individuals of all genders. Timely and accurate lung cancer detection is critical for improving cancer patients’ treatment outcomes and survival rates. Screening examinations for lung cancer detection, however, frequently fall short of detecting small polyps and cancers. To address these limitations, computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients alike.… More >

  • Open Access

    ARTICLE

    Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

    Saad Akbar, Humera Azam, Sulaiman Sulmi Almutairi, Omar Alqahtani, Habib Shah, Aliya Aleryani
    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1075-1104, 2024, DOI:10.32604/cmc.2024.050913
    (This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
    Abstract The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools. In this article, a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19, pneumonia, and normal conditions in chest X-ray images (CXIs) is proposed coupled with Explainable Artificial Intelligence (XAI). Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3, VGG16, and VGG19 that excel in the task of feature extraction. The methodology is further enhanced by the inclusion of the t-SNE (t-Distributed… More >

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