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  • Open Access

    ARTICLE

    Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

    Hassen Louati1,2, Ali Louati3,*, Elham Kariri3, Slim Bechikh2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2519-2547, 2024, DOI:10.32604/cmes.2023.030806

    Abstract Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues, particularly in the field of lung disease diagnosis. One promising avenue involves the use of chest X-Rays, which are commonly utilized in radiology. To fully exploit their potential, researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems. However, constructing and compressing these systems presents a significant challenge, as it relies heavily on the expertise of data scientists. To tackle this issue, we propose an automated approach that utilizes an evolutionary algorithm (EA) to optimize the design and compression More >

  • Open Access

    ARTICLE

    Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

    Qixin Lan1, Binqiang Chen1,*, Bin Yao1, Wangpeng He2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2825-2844, 2024, DOI:10.32604/cmes.2023.030378

    Abstract The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when… More >

  • Open Access

    ARTICLE

    Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space

    Mudassir Khalil1, Muhammad Imran Sharif2,*, Ahmed Naeem3, Muhammad Umar Chaudhry1, Hafiz Tayyab Rauf4,*, Adham E. Ragab5

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2031-2047, 2023, DOI:10.32604/cmc.2023.043687

    Abstract Early detection of brain tumors is critical for effective treatment planning. Identifying tumors in their nascent stages can significantly enhance the chances of patient survival. While there are various types of brain tumors, each with unique characteristics and treatment protocols, tumors are often minuscule during their initial stages, making manual diagnosis challenging, time-consuming, and potentially ambiguous. Current techniques predominantly used in hospitals involve manual detection via MRI scans, which can be costly, error-prone, and time-intensive. An automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest phases. This research… More >

  • Open Access

    ARTICLE

    Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

    Heba M. El-Hoseny1,*, Heba F. Elsepae2, Wael A. Mohamed2, Ayman S. Selmy2

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1855-1872, 2023, DOI:10.32604/cmc.2023.042107

    Abstract Diabetic retinopathy is a critical eye condition that, if not treated, can lead to vision loss. Traditional methods of diagnosing and treating the disease are time-consuming and expensive. However, machine learning and deep transfer learning (DTL) techniques have shown promise in medical applications, including detecting, classifying, and segmenting diabetic retinopathy. These advanced techniques offer higher accuracy and performance. Computer-Aided Diagnosis (CAD) is crucial in speeding up classification and providing accurate disease diagnoses. Overall, these technological advancements hold great potential for improving the management of diabetic retinopathy. The study’s objective was to differentiate between different classes… More >

  • Open Access

    ARTICLE

    Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model

    Chia-Wei Jan1, Yu-Jhih Chiu1, Kuan-Lin Chen2, Ting-Chun Yao3, Ping-Huan Kuo1,4,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2989-3010, 2023, DOI:10.32604/csse.2023.042078

    Abstract Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of… More >

  • Open Access

    ARTICLE

    Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System

    Nojood O Aljehane*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3109-3126, 2023, DOI:10.32604/csse.2023.038042

    Abstract Medical image analysis is an active research topic, with thousands of studies published in the past few years. Transfer learning (TL) including convolutional neural networks (CNNs) focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance. It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time. This study develops an Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System (ETSOTL-MIAS). The goal of the ETSOTL-MIAS technique lies in… More >

  • Open Access

    ARTICLE

    Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

    Fayaz Muhammad1, Jahangir Khan1, Asad Ullah1, Fasee Ullah1, Razaullah Khan2, Inayat Khan2, Mohammed ElAffendi3, Gauhar Ali3,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.038748

    Abstract IIF (Indirect Immune Florescence) has gained much attention recently due to its importance in medical sciences. The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases. The use of IIF for detecting autoimmune diseases is widespread in different medical areas. Nearly 80 different types of autoimmune diseases have existed in various body parts. The IIF has been used for image classification in both ways, manually and by using the Computer-Aided Detection (CAD) system. The data scientists conducted various research works using an automatic CAD system with low accuracy. The… More >

  • Open Access

    ARTICLE

    Micro-Expression Recognition Based on Spatio-Temporal Feature Extraction of Key Regions

    Wenqiu Zhu1,2, Yongsheng Li1,2, Qiang Liu1,2,*, Zhigao Zeng1,2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1373-1392, 2023, DOI:10.32604/cmc.2023.037216

    Abstract Aiming at the problems of short duration, low intensity, and difficult detection of micro-expressions (MEs), the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature extraction. Based on traditional convolution neural network (CNN) and long short-term memory (LSTM), a recognition method combining global identification attention network (GIA), block identification attention network (BIA) and bi-directional long short-term memory (Bi-LSTM) is proposed. In the BIA, the ME video frame will be cropped, and the training will be carried out by cropping into 24 identification blocks (IBs), 10 IBs… More >

  • Open Access

    ARTICLE

    HybridHR-Net: Action Recognition in Video Sequences Using Optimal Deep Learning Fusion Assisted Framework

    Muhammad Naeem Akbar1,*, Seemab Khan2, Muhammad Umar Farooq1, Majed Alhaisoni3, Usman Tariq4, Muhammad Usman Akram1

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3275-3295, 2023, DOI:10.32604/cmc.2023.039289

    Abstract The combination of spatiotemporal videos and essential features can improve the performance of human action recognition (HAR); however, the individual type of features usually degrades the performance due to similar actions and complex backgrounds. The deep convolutional neural network has improved performance in recent years for several computer vision applications due to its spatial information. This article proposes a new framework called for video surveillance human action recognition dubbed HybridHR-Net. On a few selected datasets, deep transfer learning is used to pre-trained the EfficientNet-b0 deep learning model. Bayesian optimization is employed for the tuning of… More >

  • Open Access

    ARTICLE

    Deep-Net: Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

    Muhammad Attique Khan1,2, Reham R. Mostafa3, Yu-Dong Zhang2, Jamel Baili4, Majed Alhaisoni5, Usman Tariq6, Junaid Ali Khan1, Ye Jin Kim7, Jaehyuk Cha7,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3029-3047, 2023, DOI:10.32604/cmc.2023.038838

    Abstract Manual diagnosis of brain tumors using magnetic resonance images (MRI) is a hectic process and time-consuming. Also, it always requires an expert person for the diagnosis. Therefore, many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature. This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm. NasNet-Mobile, a pre-trained deep learning model, has been fine-tuned and two-way trained on original and enhanced MRI images. The haze-convolutional neural network (haze-CNN) approach is developed and employed on the… More >

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