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 - 21 April 2022

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… More >

Ahmad Alassaf, Mohamed Yacin Sikkandar^*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5273-5285, 2022, DOI:10.32604/cmc.2022.025577 - 21 April 2022

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… 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 - 14 January 2022

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… 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 - 29 November 2021

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… 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 - 27 September 2021

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… More >

G. Reshma^1,*, Chiai Al-Atroshi², Vinay Kumar Nassa³, B.T. Geetha⁴, Gurram Sunitha⁵, Mohammad Gouse Galety⁶, S. Neelakandan⁷

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 621-634, 2022, DOI:10.32604/iasc.2022.019117 - 03 September 2021

Abstract In recent years, intelligent automation in the healthcare sector becomes more familiar due to the integration of artificial intelligence (AI) techniques. Intelligent healthcare systems assist in making better decisions, which further enable the patient to provide improved medical services. At the same time, skin lesion is a deadly disease that affects people of all age groups. Skin lesion segmentation and classification play a vital part in the earlier and precise skin cancer diagnosis by intelligent systems. However, the automated diagnosis of skin lesions in dermoscopic images is challenging because of the problems such as artifacts… More >

Imran Ahmed^1,*, Humaira Sardar¹, Hanan Aljuaid², Fakhri Alam Khan¹, Muhammad Nawaz¹, Adnan Awais¹

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3365-3381, 2021, DOI:10.32604/cmc.2021.018486 - 24 August 2021

Abstract Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can… More >

Charles Arputham^1,*, Krishnaraj Nagappan², Lenin Babu Russeliah³, AdalineSuji Russeliah⁴

Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 747-759, 2021, DOI:10.32604/iasc.2021.012077 - 01 March 2021

Abstract Breast cancer detection is a crucial topic in the healthcare sector. Breast cancer is a major reason for the increased mortality rate in recent years among women, specifically in developed and underdeveloped countries around the world. The incidence rate is less in India than in developed countries, but awareness must be increased. This paper focuses on an efficient deep learning-based diagnosis and classification technique to detect breast cancer from mammograms. The model includes preprocessing, segmentation, feature extraction, and classification. At the initial level, Laplacian filtering is applied to identify the portions of edges in mammogram… More >

H. Asha Gnana Priya¹, J. Anitha¹, Daniela Elena Popescu², Anju Asokan¹, D. Jude Hemanth¹, Le Hoang Son^3,4,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2771-2786, 2021, DOI:10.32604/cmc.2021.012907 - 28 December 2020

Abstract Diabetic Retinopathy (DR) is an eye disease that mainly affects people with diabetes. People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage. Once the vision is lost, it cannot be regained but can be prevented from causing any further damage. Early diagnosis of DR is required for preventing vision loss, for which a trained ophthalmologist is required. The clinical practice is time-consuming and is not much successful in identifying DR at early stages. Hence, Computer-Aided Diagnosis (CAD) system is a suitable More >

Phong Thanh Nguyen¹, Vy Dang Bich Huynh², Khoa Dang Vo¹, Phuong Thanh Phan¹, Eunmok Yang^3,*, Gyanendra Prasad Joshi⁴

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2815-2830, 2021, DOI:10.32604/cmc.2021.012315 - 28 December 2020

Abstract Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification.… More >