Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification on HAM10000 dataset largescale data.

A menu based multiple chronic disease detection system which will detect if a person is suffering from a severe disease by taking an essential input image.

Skin Cancer Detection Web App using Flask Framework deployed on the Heroku server.

Build a CNN based model which can accurately detect melanoma

🎗 This is an Android app to detect melanoma skin cancer using tensorflow mobile.

3-layered approach to detecting cancer, melanoma and allergies with state-of-the-art Tensorflow models, integrated into an app with exciting features using Flutter Android development framework.

A Web Application that uses Computer Vision and Deep Learning to identify the three highest probability diagnoses for a skin lesion image input by the User.

The dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. The objective to build deep learning model to classify given query image into one of the 7 different classes of skin cancer.

ONCO is a cancer diagnosis/prognosis mobile application focused on the 3 main cancers of the thoracic region (Breast, Lung & Skin)

This app contains and skin cancer android app whose model is created using transfer learning with inception_v3

We attempt to change how you look at Medical Diagnosis

Skin Cancer Detection project is a web application developed to detect skin cancer utilizing deep learning techniques.

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.

Skin lesion classification, using Keras and the ISIC 2020 dataset

Multiclass skin cancer detection using explainable AI for checking the models' robustness

A web app to detect Skin cancer using pictures of moles and other marks on skin

A mobile skin cancer detection application that won an international price of $10,000

Pretraining and finetuning different vision transformer models on the ImageNet and Ham10000 dataset

Predict your diseases based on the symptoms provided And Image Processing technique is used to predict the skin cancer