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Deep Learning in Medical Imaging-Disease Segmentation and Classification

Submission Deadline: 22 June 2024 Submit to Special Issue

Guest Editors

Dr. Tallha Akram, COMSATS University Islamabad, Pakistan.
Dr. Muhammad Attique Khan, HITEC University, Pakistan.
Prof. Yu-dong Zhang, University of Leicester, UK.

Summary

Deep learning has shown a huge interest in computer vision in the last few years, especially for the application of medical imaging. In medical imaging, deep learning is employed for detecting and classifying cancers such as skin cancer, stomach cancer, brain tumor, and a few more. Dermoscopy, wireless capsule endoscopy, and MRI imaging technologies are adopted for these cancers. The manual inspection of these cancers is not an easy process and always requires an expert person; therefore, a computerized method is widely required. A computerized technique employs preprocessing, segmentation, feature extraction, selection, and classification steps. The contrast enhancement using deep learning techniques is useful when a fully automated system is designed. In addition, lesion segmentation is performed using custom CNN models, U-NET, and deep saliency maps. For feature extraction, deep learning based features are extracted. Several pre-trained models, GAN, residual networks, and LSTM models are adopted for this. A deep learning model requires a large amount of data for the training process; therefore, GANs are mostly employed for data augmentation in recent years. Recently, federated learning has shown much success in the classification process in medical imaging. Furthermore, the features fusion and selection process improved the performance of the proposed framework and reduced the testing time. 


Keywords

Skin cancer
Stomach Cancer
Maternal Fetal
Lung Cancer
Oral Cancer
Breast Cancer
Explainable AI ( GradCAM, LIME,)
Residual blocks
Pre-trained Deep Learning Networks
Fusion using CCA
Federated Learning based Training
Feature Optimization
Classification

Published Papers


  • Open Access

    ARTICLE

    MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

    Tao Zhou, Yujie Guo, Caiyue Peng, Yuxia Niu, Yunfeng Pan, Huiling Lu
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050767
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot. However, there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images. A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper. The main innovations are as follows: Firstly, the Multi-scale Residual Feature Extraction Module (MRFEM) is designed to effectively extract multi-scale features. The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively. Secondly, the Multi-scale Directional Feature Perception Module… More >

  • Open Access

    ARTICLE

    Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm, Selected Deep and Handcrafted Features

    Imad Saud Al Naimi, Syed Alwee Aljunid Syed Junid, Muhammad lmran Ahmad, K. Suresh Manic
    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2585-2608, 2024, DOI:10.32604/cmc.2024.046461
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Augmentation of abnormal cells in the brain causes brain tumor (BT), and early screening and treatment will reduce its harshness in patients. BT’s clinical level screening is usually performed with Magnetic Resonance Imaging (MRI) due to its multi-modality nature. The overall aims of the study is to introduce, test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans, facilitating improved accuracy. The research intends to devise a reliable framework for detecting the BT region in the two-dimensional (2D) MRI slice, and identifying its class with improved accuracy. The methodology for the… More >

  • Open Access

    ARTICLE

    Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

    Fayez Alfayez
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1539-1560, 2024, DOI:10.32604/cmc.2024.046443
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spine fractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picture segmentation, feature reduction, and image classification. Two important elements are investigated to reduce the classification time: Using feature reduction software and leveraging the capabilities of sophisticated digital processing hardware. The researchers use different algorithms for picture enhancement, including the Wiener and Kalman filters, and they look into two background correction techniques. The article presents a technique for extracting textural features and evaluates three picture segmentation algorithms and three… More >

  • Open Access

    ARTICLE

    Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

    Umar Rashid, Arfan Jaffar, Muhammad Rashid, Mohammed S. Alshuhri, Sheeraz Akram
    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3377-3390, 2024, DOI:10.32604/cmc.2024.046320
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and… More >

  • Open Access

    ARTICLE

    Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

    Afnan M. Alhassan
    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2207-2223, 2024, DOI:10.32604/cmc.2024.046427
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BAC detection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has been formed by Enhanced Wolf… More >

  • Open Access

    ARTICLE

    Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

    Zahid Farooq Khan, Muhammad Ramzan, Mudassar Raza, Muhammad Attique Khan, Khalid Iqbal, Taerang Kim, Jae-Hyuk Cha
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1207-1225, 2024, DOI:10.32604/cmc.2023.045491
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Accurate detection and classification of artifacts within the gastrointestinal (GI) tract frames remain a significant challenge in medical image processing. Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases. Key to this is the development of robust algorithms for image classification and detection, crucial in designing sophisticated systems for diagnosis and treatment. This study makes a small contribution to endoscopic image classification. The proposed approach involves multiple operations, including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception. Additionally, feature optimization utilizes the binary dragonfly algorithm… More >

  • Open Access

    ARTICLE

    Facial Image-Based Autism Detection: A Comparative Study of Deep Neural Network Classifiers

    Tayyaba Farhat, Sheeraz Akram, Hatoon S. AlSagri, Zulfiqar Ali, Awais Ahmad, Arfan Jaffar
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 105-126, 2024, DOI:10.32604/cmc.2023.045022
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by significant challenges in social interaction, communication, and repetitive behaviors. Timely and precise ASD detection is crucial, particularly in regions with limited diagnostic resources like Pakistan. This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context. The research involves experimentation with VGG16 and MobileNet models, exploring different batch sizes, optimizers, and learning rate schedulers. In addition, the “Orange” machine learning tool is employed to evaluate classifier performance and automated… More >

  • Open Access

    ARTICLE

    Using MsfNet to Predict the ISUP Grade of Renal Clear Cell Carcinoma in Digital Pathology Images

    Kun Yang, Shilong Chang, Yucheng Wang, Minghui Wang, Jiahui Yang, Shuang Liu, Kun Liu, Linyan Xue
    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 393-410, 2024, DOI:10.32604/cmc.2023.044994
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Clear cell renal cell carcinoma (ccRCC) represents the most frequent form of renal cell carcinoma (RCC), and accurate International Society of Urological Pathology (ISUP) grading is crucial for prognosis and treatment selection. This study presents a new deep network called Multi-scale Fusion Network (MsfNet), which aims to enhance the automatic ISUP grade of ccRCC with digital histopathology pathology images. The MsfNet overcomes the limitations of traditional ResNet50 by multi-scale information fusion and dynamic allocation of channel quantity. The model was trained and tested using 90 Hematoxylin and Eosin (H&E) stained whole slide images (WSIs), which were all cropped into 320… More >

  • Open Access

    ARTICLE

    Enhancing Breast Cancer Diagnosis with Channel-Wise Attention Mechanisms in Deep Learning

    Muhammad Mumtaz Ali, Faiqa Maqsood, Shiqi Liu, Weiyan Hou, Liying Zhang, Zhenfei Wang
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2699-2714, 2023, DOI:10.32604/cmc.2023.045310
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Breast cancer, particularly Invasive Ductal Carcinoma (IDC), is a primary global health concern predominantly affecting women. Early and precise diagnosis is crucial for effective treatment planning. Several AI-based techniques for IDC-level classification have been proposed in recent years. Processing speed, memory size, and accuracy can still be improved for better performance. Our study presents ECAM, an Enhanced Channel-Wise Attention Mechanism, using deep learning to analyze histopathological images of Breast Invasive Ductal Carcinoma (BIDC). The main objectives of our study are to enhance computational efficiency using a Separable CNN architecture, improve data representation through hierarchical feature aggregation, and increase accuracy and… More >

  • Open Access

    ARTICLE

    A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

    Abdul Haseeb, Muhammad Attique Khan, Majed Alhaisoni, Ghadah Aldehim, Leila Jamel, Usman Tariq, Taerang Kim, Jae-Hyuk Cha
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3895-3920, 2023, DOI:10.32604/cmc.2023.045244
    (This article belongs to the Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
    Abstract Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the least-important features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks. This process aims to improve… More >

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