Image Classification of Brain Tumors with Deep Learning

Project Overview

This project aims to develop a deep learning model to classify brain images for tumor detection, using the dataset from Kaggle. Brain tumors can be cancerous or benign, with varying symptoms based on the affected brain area. Timely and accurate detection is crucial for effective treatment planning.

Project Goals

• Develop a model that accurately identifies brain tumors from MRI and CT scans.
• Improve diagnostic accuracy with high sensitivity and specificity to minimize false diagnoses.
• Enhance clinical decision-making through automated, reliable image analysis.

Methodology

Data Preprocessing

• Visualization, handling duplicates and corrupt images.
• Normalization, shuffling, and splitting of data for training and testing.

Model Development

• Using convolutional neural networks (CNNs) to extract and classify image features.
• Architecture includes layers for convolution, pooling, dropout, and dense connections for classification.

Training and Evaluation

• Training with stochastic gradient descent (SGD) or Adam optimizer.
• Evaluation using accuracy, precision, recall, and F1 score metrics.

Custom CNN Model

• Achieved a high training accuracy, with detailed evaluation metrics available.
• Model loss, confusion matrix, and other statistics indicate robust performance.

Transfer Learning with VGG-16

• Adapted VGG-16 architecture to classify brain tumors effectively.
• Utilized model augmentation and fine-tuning techniques for enhanced performance.