• Ravi Kumar
• Vivek kumar
• Shlok baranwal
• Rohan nadgir
• Pushpalatha S. Nikkam

The detection and classification of brain tumor play a crucial role in medical diagnosis and treatment planning. In this study, we explore various machine learning approaches to enhance the efficiency and accuracy of brain tumor detection. Firstly, we propose the utilization of Artificial Neural Networks and Convolution Neural Networks to leverage deep learning algorithms for improved predictive capabilities in brain tumor identification. Secondly, we introduce a fuzzy-based control theory technique, known as Fuzzy Inference System , for segmenting and classifying Magnetic Resonance Imaging brain images, with a particular focus on tumor identification, which holds promise for surgical image analysis. Furthermore, we present a novel Fully Automatic Heterogeneous Segmentation using Support Vector Machine algorithm designed for brain tumor segmentation, demonstrating an impressive 88.51% accuracy in differentiating abnormal and normal tissue within Magnetic Resonance Imaging images. Lastly, we discuss the application of LBF SVM for brain tumor detection, catering to radiologists and neurologists by providing a user-friendly graphical interface. Experimental results indicate that the system optimizes medical professionals' performance in identifying brain diseases. Through these advancements, we aim to contribute to the improvement of diagnostic accuracy and treatment efficacy in neurology, ultimately benefiting patients' well-being.

Artificial Neural Networks (ANN), Convolution Neural Networks (CNN), Fuzzy Inference System (FIS), Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM), Magnetic Resonance Imaging (MRI), Genetic Algorithms (GA), Conditional Random Fields (CRF), Least-Feature-Specific Support Vector Machine(LF SVM).

"Identification and detction of brain tumor", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.a725-a728, April-2024, Available :http://www.jetir.org/papers/JETIR2404089.pdf

"Identification and detction of brain tumor", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppa725-a728, April-2024, Available at : http://www.jetir.org/papers/JETIR2404089.pdf