/Skin-Cancer-Detection

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Skin-Cancer-Detection

Skin cancer is a prevalent form of cancer that affects millions of people worldwide. Early detection and accurate prediction of skin cancer can significantly improve patient outcomes and increase the chances of successful treatment. In recent years, machine learning techniques have shown promising results in skin cancer prediction based on various clinical and dermoscopic features. This abstract presents an overview of skin cancer prediction using machine learning algorithms. The the study focuses on the analysis and classification of skin lesion images obtained through non-invasive imaging techniques. A comprehensive dataset comprising a diverse range of benign and malignant skin lesions are utilized for training and evaluation. Various image processing techniques are employed to extract relevant features from the skin lesion images, such as color, texture, shape, and asymmetry.
Link to access the website: https://threesquaree.github.io/Skin-Cancer-Prediction-using-CNN-Model/

Objective

The objective of the project is to develop a machine learning model that can accurately classify skin images as either cancerous or non-cancerous. The project aims to leverage the power of convolutional neural networks (CNNs), a type of deep learning algorithm specifically designed for image recognition tasks.
The primary goals of this project include:

  • Early Detection: Skin cancer, if detected early, can significantly increase the chances of successful treatment. The objective is to develop a model that can detect skin cancer at an early stage by analyzing images of skin lesions.
  • Accuracy and Reliability: The model should achieve high accuracy in classifying skin images as cancerous or non-cancerous. It should be able to handle different types of skin lesions and provide reliable results. Automation: Automating the skin cancer detection process using a CNN model can potentially assist dermatologists and medical professionals in their diagnosis. The objective is to create a tool that can augment their expertise and provide efficient and consistent results.
  • Accessibility: By utilizing deep learning and CNNs, the project aims to develop an accessible and cost-effective solution for skin cancer detection. This can help make skin cancer screening more widely available, especially in areas where access to specialized medical professionals is limited.

    Dataset

    The datasets that we are using for the project has been taken from kaggle (Detailed Images of Skin Lesions).
    Link : https://www.kaggle.com/datasets/fanconic/skin-cancer-malignant-vs-benign

    Methodology

    Steps of the system Neural Network diagram

    GUI