A Convolutional neural network (CNN)model to train and detect skin cancer (benign and malignant) disease using DDI(Diverse Dermatology Images)Dataset.
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Updated
Sep 3, 2023 - Jupyter Notebook
A Convolutional neural network (CNN)model to train and detect skin cancer (benign and malignant) disease using DDI(Diverse Dermatology Images)Dataset.
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.
A skin cancer prediction convolutional deep-learning model.
Exploring the effects of class ordering on model performance to gain insight into optimal task ordering for devising a curriculum for continual learning
Skin Cancer Detection: Leveraging Hybrid Deep Learning Models and Traditional Machine Learning Classifiers
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 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 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 is for Final Project of Theoretical Machine Learning Course...
Skin Cancer Classification
Skin cancer detection using CNNs on custom Kaggle dataset.
Skin Cancer Detection project is a web application developed to detect skin cancer utilizing deep learning techniques.
Creating a possible model to detect melanoma from the dataset accurately using CNN.
Skin Lesion Classifier: a skin lesion analysis towards melanoma detection.
Explainable AI (XAI) project aiming to show undesired bias in skin cancer predictions models trained on the ISIC dataset
A web app to detect Skin cancer using pictures of moles and other marks on skin
Implementing and comparing ResNet50 and MobileNetV2 transfer learning models using the MNIST:HAM10000 image dataset. Resulting classification accuracy of ~90%.
Pretraining and finetuning different vision transformer models on the ImageNet and Ham10000 dataset
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