Enhancing Therapeutic Investigation Through AI Driven Convolutional Neural Network in Comparison with Deep Learning Techniques
Year: 2025
Type: article
Abstract: This study seeks to enhance an Artificial Intelligence (AI) system for identifying medical issues using deep learning (DL) techniques. Conventional methods often struggle to predict health conditions and provide effective solutions. A re-modelled convolutional neural network (RCNN) is introduced, featuring optimized activation functions in its convolutional layers and incorporating dense, fully connected layers. The efficiency of the RCNN algorithm is validated by comparing it with other advanced deep learning algorithms. Using available datasets, the study evaluates the accuracy of the DL system in detecting medical conditions within the Python Jupyter environment. Performance metrics, including F1 score, recall, accuracy, and precision, are used to assess the effectiveness of the proposed RCNN model.
Source: Journal of Machine and Computing
Authors: Mohammad Azhar, Mary Gladence L
Institution: Sathyabama Institute of Science and Technology
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4409184271