The Evaluation of 2D and EfficientB0 Convolution Networks for detecting Brain tumor based on MRI images

¹ Department of AI&ML, KG Reddy College of Engineering and Technology, Moinabad, Hyderabad, Telangana, India.
² Department of Information Technology, GRIET, Hyderabad, Telangana, India
³ Department of IT, GRIET, Hyderabad, Telangana, India
⁴ Lovely Professional University, Phagwara, Punjab, India.

^* Corresponding author: subbu.kgrcet@gmail.com

Abstract

Brain tumors represent a significant healthcare challenge, affecting both children and adults with potentially aggressive consequences. Accounting for a substantial percentage of all primary Central Nervous System (CNS) tumors, brain tumors pose a substantial burden, with approximately 11,700 new diagnoses annually. The classification of brain tumors into benign, malignant, pituitary, and other types necessitates precise diagnostic techniques and treatment planning to enhance patient life expectancy. Traditionally, the detection of brain tumors relied on the expertise of specialists analyzing Magnetic Resonance Images (MRI) without the aid of advanced technology. MRI remains the gold standard for brain tumor detection, generating vast amounts of image data for radiologists to interpret. Manual examinations, however, carry a risk of errors due to the intricacies and diverse properties of brain tumors, potentially leading to delayed treatment and, tragically, loss of lives. In this context, the application of automated classification techniques using Machine Learning (ML) and Deep Learning (DL) has emerged as a promising solution. These techniques, primarily employing Deep Learning Algorithms such as Convolutional Neural Networks (CNN) like 2D-convolutions and Deep Learning Models like ResNet50 and EfficientNetB0, in addition to traditional Machine Learning algorithms like Support Vector Machines (SVM), have consistently demonstrated superior accuracy in brain tumor detection compared to manual prediction. These automated methods have consistently exhibited superior accuracy in the detection and classification of brain tumors compared to manual approaches. This research proposes a robust system for the early detection and accurate classification of brain tumors, leveraging the power of Deep Learning and Machine Learning. By incorporating state-of-the-art techniques, this system aims to empower medical professionals worldwide in identifying brain tumors at their earliest stages, ultimately leading to more timely and effective treatments. Such advancements hold great promise in reducing the human suffering associated with brain tumors and improving patient outcomes.