Melanoma Cancer Classification

Overview

Melanoma, a potentially life-threatening form of skin cancer, requires early detection for effective treatment. This project employs state-of-the-art deep learning techniques, specifically leveraging the EfficientNetB1 backbone and advanced augmentation strategies, to develop a highly accurate and robust model for automated melanoma skin lesion classification.

• EfficientNetB1 Backbone: The project harnesses the power of EfficientNetB1, a well-balanced convolutional neural network architecture, striking an optimal balance between model complexity and performance.

• Augmentation Strategies: Data augmentation techniques are implemented to enhance the model's ability to generalize across diverse skin lesion images, ensuring reliable performance in real-world scenarios.

• Early Detection: The primary goal is to enable early detection of melanoma lesions through the automated analysis of skin images, contributing to improved patient outcomes and reduced mortality rates.

Environment Setup

• Clone this repository:

git clone https://github.com/Hamana0509/Melanoma-cancer-classification.git
cd Melanoma-cancer-classification

Model Architecture

The Melanoma classification model is built on the EfficientNetB1 architecture, a state-of-the-art convolutional neural network designed for efficient and effective image classification. EfficientNetB1 strikes a balance between model size and performance, making it well-suited for tasks where computational resources may be limited. The model architecture is implemented using TensorFlow

Summary

Model Result

• Test Loss: 0.22894
• Test Accuracy: 91.70%

Review and Evaluation

• The model is performing well, as evidenced by a relatively low test loss.
• The accuracy of 91.70% suggests that the model is correctly classifying the majority of the test samples.