• Renu Saini
• Dr. J. R. Arun Kumar
• Mohit Sharma

Brain cancer is one of the most aggressive and life-threatening diseases, significantly impacting the lives of millions worldwide. Early and accurate detection is critical to improving patient outcomes, yet traditional diagnostic methods are often time-consuming, subjective, and dependent on the expertise of radiologists. The advent of deep learning, particularly convolutional neural networks (CNNs) and hybrid architectures, has revolutionized brain tumor detection by enabling automated and precise image analysis. This review explores the latest advancements in deep learning for brain cancer detection, including supervised, unsupervised, and hybrid approaches. We also discuss key challenges such as data scarcity, model interpretability, and computational complexity, as well as future directions for improving deep learning applications in medical imaging.

Brain Cancer , MRI ,CT scans , Deep Learning

"Brain Cancer Detection Using Deep Learning: A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.h553-h556, January-2025, Available :http://www.jetir.org/papers/JETIR2501751.pdf

"Brain Cancer Detection Using Deep Learning: A Comprehensive Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. pph553-h556, January-2025, Available at : http://www.jetir.org/papers/JETIR2501751.pdf