Jawad Rasheed^1,*, Shtwai Alsubai²

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4239-4259, 2023, DOI:10.32604/cmc.2023.031969

Abstract Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal… More >

Ghada Emam Atteia^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 361-376, 2023, DOI:10.32604/csse.2023.029597

Abstract Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates the proficiency of autoencoder networks… More >

Areej A. Malibari¹, Marwa Obayya², Mohamed K. Nour³, Amal S. Mehanna⁴, Manar Ahmed Hamza^5,*, Abu Sarwar Zamani⁵, Ishfaq Yaseen⁵, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4123-4138, 2022, DOI:10.32604/cmc.2022.030492

Abstract With the rapid increase of new cases with an increased mortality rate, cancer is considered the second and most deadly disease globally. Breast cancer is the most widely affected cancer worldwide, with an increased death rate percentage. Due to radiologists’ processing of mammogram images, many computer-aided diagnoses have been developed to detect breast cancer. Early detection of breast cancer will reduce the death rate worldwide. The early diagnosis of breast cancer using the developed computer-aided diagnosis (CAD) systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with… More >

Venkata Sunil Srikanth^*, S. Krithiga

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 63-78, 2023, DOI:10.32604/iasc.2023.023474

Abstract Deep neural network (DNN) based computer-aided breast tumor diagnosis (CABTD) method plays a vital role in the early detection and diagnosis of breast tumors. However, a Brightness mode (B-mode) ultrasound image derives training feature samples that make closer isolation toward the infection part. Hence, it is expensive due to a meta-heuristic search of features occupying the global region of interest (ROI) structures of input images. Thus, it may lead to the high computational complexity of the pre-trained DNN-based CABTD method. This paper proposes a novel ensemble pre-trained DNN-based CABTD method using global- and local-ROI-structures of B-mode ultrasound images. It conveys… More >

D. Banumathy^1,*, Osamah Ibrahim Khalaf², Carlos Andrés Tavera Romero³, P. Vishnu Raja⁴, Dilip Kumar Sharma⁵

Computer Systems Science and Engineering, Vol.44, No.1, pp. 595-612, 2023, DOI:10.32604/csse.2023.025611

Abstract The most salient argument that needs to be addressed universally is Early Breast Cancer Detection (EBCD), which helps people live longer lives. The Computer-Aided Detection (CADs)/Computer-Aided Diagnosis (CADx) system is indeed a software automation tool developed to assist the health professions in Breast Cancer Detection and Diagnosis (BCDD) and minimise mortality by the use of medical histopathological image classification in much less time. This paper purposes of examining the accuracy of the Convolutional Neural Network (CNN), which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early identification of… More >

Shi Qiu¹, Bin Li^2,*, Tao Zhou³, Feng Li⁴, Ting Liang⁵

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4897-4910, 2022, DOI:10.32604/cmc.2022.026855

Abstract Lung is an important organ of human body. More and more people are suffering from lung diseases due to air pollution. These diseases are usually highly infectious. Such as lung tuberculosis, novel coronavirus COVID-19, etc. Lung nodule is a kind of high-density globular lesion in the lung. Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis, which is inefficient. For this reason, the use of computer-assisted diagnosis of lung nodules has become the current main trend. In the process of computer-aided diagnosis, how to reduce the false positive… More >

Ahmad Alassaf, Mohamed Yacin Sikkandar^*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5273-5285, 2022, DOI:10.32604/cmc.2022.025577

Abstract Malaria is a severe disease caused by Plasmodium parasites, which can be detected through blood smear images. The early identification of the disease can effectively reduce the severity rate. Deep learning (DL) models can be widely employed to analyze biomedical images, thereby minimizing the misclassification rate. With this objective, this study developed an intelligent deep-transfer-learning-based malaria parasite detection and classification (IDTL-MPDC) model on blood smear images. The proposed IDTL-MPDC technique aims to effectively determine the presence of malarial parasites in blood smear images. In addition, the IDTL-MPDC technique derives median filtering (MF) as a pre-processing step. In addition, a residual… More >

Qaisar Abbas^1,*, Mostafa E. A. Ibrahim^1,2, Abdul Rauf Baig¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4573-4590, 2022, DOI:10.32604/cmc.2022.023670

Abstract Diabetic retinopathy (DR) diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features. This task is very difficult for ophthalmologists and time-consuming. Therefore, many computer-aided diagnosis (CAD) systems were developed to automate this screening process of DR. In this paper, a CAD-DR system is proposed based on preprocessing and a pre-train transfer learning-based convolutional neural network (PCNN) to recognize the five stages of DR through retinal fundus images. To develop this CAD-DR system, a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related lesions and then a standard… More >

Jing Lu¹, Yan Wu^2,#, Mingyan Hu¹, Yao Xiong¹, Yapeng Zhou¹, Ziliang Zhao¹, Liutong Shang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 365-377, 2022, DOI:10.32604/cmes.2021.017897

Abstract Background: The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue. Early diagnosis of tumors has become the most effective way to prevent breast cancer. Method: For distinguishing between tumor and non-tumor in MRI, a new type of computer-aided detection CAD system for breast tumors is designed in this paper. The CAD system was constructed using three networks, namely, the VGG16, Inception V3, and ResNet50. Then, the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system. Result: CAD system built based on VGG16, Inception… More >

S. Sathiya Devi¹, S. Vidivelli^2,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3049-3066, 2022, DOI:10.32604/cmc.2022.019917

Abstract Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer. The present research work is useful in image processing for characterizing shape and gray-scale complexity. The proposed Modified Differential Box Counting (MDBC) extract Fractal features such as Fractal Dimension (FD), Lacunarity, and Succolarity for shape characterization. In traditional DBC method, the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels. The problem is overcome by the proposed MDBC method that uses box over counting and under counting that covers the… More >