COMPARATIVE DEEP LEARNING APPROACHES FOR BRAIN TUMOR CLASSIFICATION

Abstract

Classification of brain tumors poses significant issues within the Accurate prompt detection is critical for effective treatment and optimization of patient results. The evaluation of Magnetic Resonance Imaging (MRI) scans has historically relied on the expertise of trained radiologists, which made the process labor intensive and susceptible to variability between readers. Inception of machine learning techniques, while a step in the right direction, heavily relied on handcrafted features for classification, making the process time intensive and in many cases, inaccurate., and (CNNs), brought about a revolutionary change in the analysis of medical images. These models made it possible to automate classification with high precision through learning complex patterns from images, which change the whole paradigm of diagnosing is a models based on transfer learning (TL) which apply knowledge from large datasets to address the limited in-domain medical datasets problem. Such models demonstrated remarkable effectiveness in the prediction of various medical conditions.

The shift from manually interpreting to using sophisticated deep learning models illustrates a fundamental metamorphosis in the methodology employed for medical imaging in diagnostics, moving towards more precise, accurate, and efficient methods. 

Automated and accurate brain tumor classification is a clinical need. Timely and precise diagnosis is essential for patient survival because brain tumors, a leading worldwide cause of morbidity and mortality, require prompt and accurate diagnosis. Automated systems can improve efficiency and diminish human error and variability among radiologists, improving the clinical workflow in the diagnostic imaging centers. These deep learning models are intelligent and scalable and can serve as robust decision support systems, providing guidance in clinical and therapeutic decision-making and thereby improving patient outcomes through timely interventions. This clinical need creates the impetus for the evolution of automated classification systems, thereby framing this research within the challenging and important area of health care.

The aim of this research is to perform an exhaustive comparison of three different deep learning techniques to classify binary brain tumors (benign versus malignant) with the Br35h-Preprocessed dataset. The approaches selected for this study include a two pre-trained EfficientNetB0 InceptionV3. Every assessed concerning the dataset's architecture, implementation, handling of data, regularization techniques, and results achieved. The custom CNN provided a valuable baseline for the custom model, but it was limited in its ability to generalize, as it only achieved a validation accuracy of 93.00% because of the lack of data augmentation. EfficientNetB0 and InceptionV3, however, performed exceptionally well because of the transfer learning with strong regularization strategies. The EfficientNetB0 model, with a fine-tuning strategy on its upper layers, achieved of 99.56% of 100%. The fully fine-tuned InceptionV3 model, with advanced data augmentation and aggressive label smoothing techniques, tested at 98.67%. The results show that transfer learning is an effective solution for this particular case of classifying images in the medical domain. The conclusion is that while both models performed superbly, EfficientNetB0 edged ahead on both speed and ease of computation.