Melanoma Skin Cancer Detection using Convolutional Neural Networks and Transfer Learning

This is a Kaggle competition in which we have to identify if the given lesion image is malignant or not for Melanoma which is a type of skin cancer.

📝 Table of Contents

🧐 About
📊 Dataset Overview
🧠 Model Building
🎯 Model Performance
🏅 Model Evaluation

🧐 About

Melanoma is a type of skin cancer that occurs when pigment-producing cells (melanocytes) mutate and become cancerous. Melanoma is the most serious form of skin cancer and 5th most common cancer.

The American Cancer Society estimates around 207,390 new melanoma cases will be diagnosed in 2021. As with other cancers, early and accurate detection—potentially aided by data science—can make treatment more effective.

Currently, dermatologists evaluate every one of a patient's moles to identify outlier lesions that are most likely to be melanoma.

📊 Dataset Overview

The dataset was taken from the competition hosted by SIIM-ISIC on Kaggle.

Malignant: A cancerous growth that can spread very quickly and invade nearby tissues
Benign: A non-cancerous growth that usually grows very slowly and does not spread to other areas

Click to view 👇 :

🧠 Model Building

Here, the bottom layers of pre-trained Xception model were used and the top layers were fine-tuned for our particular task to classify skin cancer images as malignant or not. The architecture of xception model is as follows:

🎯 Model Performance

Since the dataset is highly imbalanced, accuracy wouldn't be a good metric to judge the model performnace hence we have used the AOC as a performance metric along with BinaryCrossentropy as the loss function. Here is the model's performance.

🏅 Model Evaluation

We evaluated our model on the test dataset and we were able to achieve an AUC of 0.8459. Here is the curve:

Melanoma Skin Cancer Detection using Convolutional Neural Networks and Transfer Learning🕵🏻‍♂️

Related tags

Overview

Melanoma Skin Cancer Detection using Convolutional Neural Networks and Transfer Learning

📝 Table of Contents

🧐 About

📊 Dataset Overview

Click to view 👇 :

🧠 Model Building

🎯 Model Performance

🏅 Model Evaluation

Click to view 👇 :

Thank you!

Owner

Vipul Shinde

A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV

ICCV2021: Code for 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'

MediaPipe is a an open-source framework from Google for building multimodal

A flexible and extensible framework for gait recognition.

A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

Python Implementation of Chess Playing AI with variable difficulty

Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning

Attention Probe: Vision Transformer Distillation in the Wild

Tool for installing and updating MiSTer cores and other files

Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready inference.

Collects many various multi-modal transformer architectures, including image transformer, video transformer, image-language transformer, video-language transformer and related datasets

(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

This repository contains the code for EMNLP-2021 paper "Word-Level Coreference Resolution"

BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation

Kaggle-titanic - A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

Official implementation of "StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation" (SIGGRAPH 2021)

The first public PyTorch implementation of Attentive Recurrent Comparators

[ICCV2021] Official Pytorch implementation for SDGZSL (Semantics Disentangling for Generalized Zero-Shot Learning)

Official PyTorch implementation of "AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks"

This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation)