Elias Hossain¹, Abdulwahab Alazeb^2,*, Naif Al Mudawi², Sultan Almakdi², Mohammed Alshehri², M. Gazi Golam Faruque³, Wahidur Rahman³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4945-4966, 2022, DOI:10.32604/cmc.2022.027058

Abstract Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology of this proposed study is… More >

Elias Hossain¹, Md. Shazzad Hossain², Md. Selim Hossain³, Sabila Al Jannat⁴, Moontahina Huda⁵, Sameer Alsharif⁶, Osama S. Faragallah⁷, Mahmoud M. A. Eid⁸, Ahmed Nabih Zaki Rashed^9,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4509-4523, 2022, DOI:10.32604/cmc.2022.025977

Abstract Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate… More >

Elias Hossain¹, Mohammed Alshehri², Sultan Almakdi^2,*, Hanan Halawani², Md. Mizanur Rahman³, Wahidur Rahman⁴, Sabila Al Jannat⁵, Nadim Kaysar⁶, Shishir Mia⁴

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1713-1746, 2022, DOI:10.32604/cmc.2022.024822

Abstract Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering Question Module is a natural… More >