An Efficient GPU/Deep Learning Model Approach for Brain Tumor Detection in Pakistan

Abstract

The detection and diagnosis of brain tumors using conventional methods have enormous limitations and ambiguities. Purpose of this study is to identify Brain Tumor (BT) in CT scan by using emerging artificial intelligence paradigm i.e deep learning models. The primary objective is to leverage deep learning to advance the development of robust and reliable tools for early detection and diagnosis of brain tumors. Conventional methods for BT detection are no longer sufficient. and suitable approach for BT detection, as it is very sensitive and critical for human. So this study put an effort to evaluate the performance of deep learning models in recognizing BT in CT scans, with an additional focus on the development of a user-friendly dashboard using PHP for result visualization. The results of this research will contribute to the development of trustworthy tools that can aid medical professionals in the early detection and diagnosis of BT. To validate the effectiveness of the deep learning model, a comprehensive experimental evaluation is conducted using publicly accessible brain tumor datasets. The model's accuracy, sensitivity, specificity, and other relevant performance measures are rigorously assessed. Additionally, the study introduces a user-friendly dashboard developed in PHP to facilitate the intuitive display of results, enhancing the practicality of the deep learning model in a clinical setting. The experimental evaluation, using a substantial dataset of annotated BT images, confirms the effectiveness of the deep learning models in recognizing brain tumors in CT scans. The study provides valuable insights into the functionality, interpretability, and potential clinical application of the deep learning models for diagnosing brain tumors. This research contributes to ongoing efforts in BT treatment, while also aiming to improve patient care and outcomes.