Omar M. El-Habbak¹, Abdelrahman M. Abdelalim¹, Nour H. Mohamed¹, Habiba M. Abd-Elaty¹, Mostafa A. Hammouda¹, Yasmeen Y. Mohamed¹, Mohanad A. Taifor¹, Ali W. Mohamed^2,3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2953-2969, 2022, DOI:10.32604/cmc.2022.020109 - 27 September 2021

Abstract Parkinson’s disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’ voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers. Results were highly successful, with 90% accuracy produced by the random 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 >

Thejovathi Murari¹, L. Prathiba², Kranthi Kumar Singamaneni^3,*, D. Venu⁴, Vinay Kumar Nassa⁵, Rachna Kohar⁶, Satyajit Sidheshwar Uparkar⁷

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1241-1256, 2022, DOI:10.32604/iasc.2022.020203 - 22 September 2021

Abstract In recent times, big data analytics using Machine Learning (ML) possesses several merits for assimilation and validation of massive quantity of complicated healthcare data. ML models are found to be scalable and flexible over conventional statistical tools, which makes them suitable for risk stratification, diagnosis, classification and survival prediction. In spite of these benefits, the utilization of ML in healthcare sector faces challenges which necessitate massive training data, data preprocessing, model training and parameter optimization based on the clinical problem. To resolve these issues, this paper presents new Big Data Analytics with Optimal Elman Neural… More >

Sami Gazzah^1,*, Rida Bayi², Soulaimane Kaloun², Omar Bencharef²

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 677-692, 2022, DOI:10.32604/iasc.2022.019360 - 22 September 2021

Abstract A new virus called SARS-CoV-2 appeared at the end of space 2019 in Wuhan, China. This virus immediately spread throughout the world due to its highly contagious nature. Moreover, SARS-CoV-2 has changed the way of our life and has caused a huge economic and public health disaster. Therefore, it is urgent to identify positive cases as soon as possible and treat them as isolated. Automatic detection of viruses using computer vision and machine learning will be a valuable contribution to detecting and limiting the spread of this epidemic. The delay introduction of X-ray technology as… More >

Schahrazad Soltane^*, Sameer Alsharif , Salwa M.Serag Eldin

Computer Systems Science and Engineering, Vol.40, No.2, pp. 629-644, 2022, DOI:10.32604/csse.2022.019333 - 09 September 2021

Abstract Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification. Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells. However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem. In literature, many attempts were made to classify up to four simple types of lymphoma. This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and… More >

G. Jayandhi^1,*, J.S. Leena Jasmine², S. Mary Joans²

Computer Systems Science and Engineering, Vol.40, No.2, pp. 491-503, 2022, DOI:10.32604/csse.2022.016376 - 09 September 2021

Abstract The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces… More >

Ahmed Reda^*, Sherif Barakat, Amira Rezk

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1381-1399, 2022, DOI:10.32604/cmc.2022.019809 - 07 September 2021

Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a More >

M. Buvana¹, K. Muthumayil², S. Senthil kumar³, Jamel Nebhen⁴, Sultan S. Alshamrani⁵, Ihsan Ali^6,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 43-58, 2022, DOI:10.32604/cmc.2022.019331 - 07 September 2021

Abstract Coronavirus (COVID-19) outbreak was first identified in Wuhan, China in December 2019. It was tagged as a pandemic soon by the WHO being a serious public medical condition worldwide. In spite of the fact that the virus can be diagnosed by qRT-PCR, COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray (CXR) and Computed Tomography (CT) images. In this paper, the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features. Impressive features like Speeded-Up… More >

Mohamed Esmail Karar^1,2, Marwa Ahmed Shouman³, Claire Chalopin^4,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1683-1697, 2022, DOI:10.32604/cmc.2022.018564 - 07 September 2021

Abstract The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient lives worldwide daily. Although Chest X-ray and Computed Tomography are the gold standard medical imaging modalities for diagnosing potentially infected COVID-19 cases, applying Ultrasound (US) imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently. In this article, we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images, based on generative adversarial neural networks (GANs). The proposed image classifiers are a semi-supervised GAN and a modified GAN with… More >

C. S. S. Anupama¹, L. Natrayan², E. Laxmi Lydia³, Abdul Rahaman Wahab Sait⁴, José Escorcia-Gutierrez⁵, Margarita Gamarra^6,*, Romany F. Mansour⁷

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1297-1313, 2022, DOI:10.32604/cmc.2022.018396 - 07 September 2021

Abstract Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized in the development of smart healthcare systems. These entities can support real-time applications by exploiting massive volumes of data, produced by wearable sensor devices. The advent of evolutionary computation algorithms and Deep Learning (DL) models has gained significant attention in healthcare diagnosis, especially in decision making process. Skin cancer is the deadliest disease which affects people across the globe. Automatic skin lesion classification model… More >