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This project reach to create an application that allows to detect if a spot on the skin is carcinogenic using different pattern recognition techniques to identify the main characteristics of the spot.
Exploring the effects of class ordering on model performance to gain insight into optimal task ordering for devising a curriculum for continual learning
Dermatologists suffer from the difficulty of locating cancerous and malignant skin lesions, which causes many problems during the process of removing the tumor, which leads to the return of the tumor again. In determining the location of the tumor and its spread and determining the area that must be removed accurately.
This model aims to leverage the power of machine learning and deep learning techniques to accurately detect melanoma, a serious type of skin cancer, from skin lesion images.
This project uses TensorFlow to implement a Convolutional Neural Network (CNN) for image classification. The goal is to classify skin lesion images into different categories. The dataset used is HAM10000, which contains skin lesion images with associated metadata. The actual accuracy of the model is 90%. 🚀🚀
This repository contains Python code for generating a skin cancer detection model and utilizing it to detect skin cancer from user-inputted images or videos. The model architecture follows a sequential structure consisting of convolutional and pooling layers, with the final output layer using a sigmoid activation function.