Tahani Maazi Almutairi^*, Mohamed Maher Ben Ismail, Ouiem Bchir

Journal on Artificial Intelligence, Vol.4, No.3, pp. 167-187, 2022, DOI:10.32604/jai.2022.032974 - 01 December 2022

Abstract The world has been suffering from the Coronavirus (COVID-19) pandemic since its appearance in late 2019. COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide. Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease. In this paper, we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases. Specifically, we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system. Particularly, lightweight EfficientNet networks… More >

Ahmed ElShafee¹, Walid El-Shafai², Abdulaziz Alarifi^3,*, Mohammed Amoon³, Aman Singh⁴, Moustafa H. Aly⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4157-4177, 2022, DOI:10.32604/cmc.2022.028847 - 16 June 2022

Abstract Real-time disease prediction has emerged as the main focus of study in the field of computerized medicine. Intelligent disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable, consistent, and timely, successfully lowering mortality rates, particularly during endemics and pandemics. To prevent this pandemic’s rapid and widespread, it is vital to quickly identify, confine, and treat affected individuals. The need for auxiliary computer-aided diagnostic (CAD) systems has grown. Numerous recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 virus. Utilizing advanced convolutional neural network (CNN)… More >

Yasmeen Al-Saeed^1,2, Wael A. Gab-Allah¹, Hassan Soliman¹, Maysoon F. Abulkhair³, Wafaa M. Shalash⁴, Mohammed Elmogy^1,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4871-4894, 2022, DOI:10.32604/cmc.2022.023638 - 14 January 2022

Abstract One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main More >