Cheng Wan¹, Jiani Zhao¹, Xiangqian Hong², Weihua Yang^2,*, Shaochong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 391-407, 2024, DOI:10.32604/cmc.2024.048307

Abstract Age-related macular degeneration (AMD) ranks third among the most common causes of blindness. As the most conventional and direct method for identifying AMD, color fundus photography has become prominent owing to its consistency, ease of use, and good quality in extensive clinical practice. In this study, a convolutional neural network (CSPDarknet53) was combined with a transformer to construct a new hybrid model, HCSP-Net. This hybrid model was employed to tri-classify color fundus photography into the normal macula (NM), dry macular degeneration (DMD), and wet macular degeneration (WMD) based on clinical classification manifestations, thus identifying and resolving AMD as early as… More >

Huan Wang¹, Shi Qiu^1,2,*, Benyue Zhang¹, Lixuan Xiao³

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1569-1589, 2024, DOI:10.32604/cmc.2023.046821

Abstract Lung cancer is a malady of the lungs that gravely jeopardizes human health. Therefore, early detection and treatment are paramount for the preservation of human life. Lung computed tomography (CT) image sequences can explicitly delineate the pathological condition of the lungs. To meet the imperative for accurate diagnosis by physicians, expeditious segmentation of the region harboring lung cancer is of utmost significance. We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner, erect an interpretable model, and attain segmentation of lung cancer. The specific advancements can be encapsulated as follows:… More >

Kun Yang^1,2,3, Shilong Chang¹, Yucheng Wang¹, Minghui Wang¹, Jiahui Yang¹, Shuang Liu^1,2,3, Kun Liu^1,2,3, Linyan Xue^1,2,3,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 393-410, 2024, DOI:10.32604/cmc.2023.044994

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 >

Hassen Louati^1,2, Ali Louati^3,*, 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 of a convolutional neural network… More >

Fayaz Muhammad¹, Jahangir Khan¹, Asad Ullah¹, Fasee Ullah¹, Razaullah Khan², Inayat Khan², Mohammed ElAffendi³, Gauhar Ali^3,*

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 diseases in the human body… More >

Mohammed Jasim Mohammed Jasim¹, Bzar Khidir Hussan², Subhi R. M. Zeebaree^3,*, Zainab Salih Ageed⁴

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3677-3693, 2023, DOI:10.32604/cmc.2023.037363

Abstract The major mortality factor relevant to the intestinal tract is the growth of tumorous cells (polyps) in various parts. More specifically, colonic polyps have a high rate and are recognized as a precursor of colon cancer growth. Endoscopy is the conventional technique for detecting colon polyps, and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate. The automated diagnosis of polyps in a computer-aided diagnosis (CAD) method is implemented using statistical analysis. Nowadays, Deep Learning, particularly through Convolution Neural networks… More >

Manar Ahmed Hamza^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2879-2895, 2023, DOI:10.32604/iasc.2023.034719

Abstract Breast Cancer (BC) is considered the most commonly scrutinized cancer in women worldwide, affecting one in eight women in a lifetime. Mammography screening becomes one such standard method that is helpful in identifying suspicious masses’ malignancy of BC at an initial level. However, the prior identification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classification. The advancements in the DL field paved the way for highly intellectual and… More >

M. Uvaneshwari¹, M. Baskar^2,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1811-1826, 2023, DOI:10.32604/csse.2023.035455

Abstract The Brain Tumor (BT) is created by an uncontrollable rise of anomalous cells in brain tissue, and it consists of 2 types of cancers they are malignant and benign tumors. The benevolent BT does not affect the neighbouring healthy and normal tissue; however, the malignant could affect the adjacent brain tissues, which results in death. Initial recognition of BT is highly significant to protecting the patient’s life. Generally, the BT can be identified through the magnetic resonance imaging (MRI) scanning technique. But the radiotherapists are not offering effective tumor segmentation in MRI images because of the position and unequal shape… More >

José Escorcia-Gutierrez^1,*, Roosvel Soto-Diaz², Natasha Madera³, Carlos Soto³, Francisco Burgos-Florez², Alexander Rodríguez⁴, Romany F. Mansour⁵

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1337-1353, 2023, DOI:10.32604/csse.2023.035253

Abstract Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve the feasibility and performance of CXR for TB screening and triage. At the same time, CXR interpretation is a time-consuming and subjective process. Furthermore, high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis. Therefore, computer-aided diagnosis (CAD) models using machine learning (ML) and deep learning (DL) can be designed for screening TB accurately. With this motivation, this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification… More >

Geng Qin¹, Ping Luo¹, Kaiyuan Li¹, Yufeng Sun¹, Shiwei Wang¹, Xiaoting Li^1,2,3, Shuang Liu^1,2,3, Linyan Xue^1,2,3,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 999-1015, 2023, DOI:10.32604/cmc.2023.035777

Abstract Due to small size and high occult, metacarpophalangeal fracture diagnosis displays a low accuracy in terms of fracture detection and location in X-ray images. To efficiently detect metacarpophalangeal fractures on X-ray images as the second opinion for radiologists, we proposed a novel one-stage neural network named MPFracNet based on RetinaNet. In MPFracNet, a deformable bottleneck block (DBB) was integrated into the bottleneck to better adapt to the geometric variation of the fractures. Furthermore, an integrated feature fusion module (IFFM) was employed to obtain more in-depth semantic and shallow detail features. Specifically, Focal Loss and Balanced L1 Loss were introduced to… More >