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

Related tags

Deep Learning data-science deep-learning transfer-learning biomedical-image-processing
Overview

forthebadge forthebadge forthebadge

Melanoma Skin Cancer Detection using Convolutional Neural Networks and Transfer Learning

Status License GitHub repo size

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

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.

data-overview

  • 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 👇 :

forthebadge

🧠 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:

xception-architecture

🎯 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.

live-loss-plot

performance-result

🏅 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:

auc-curve

test-data-evaluation

Click to view 👇 :

forthebadge

Thank you!

You might also like...
Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Naive-Bayes Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes Downloading Data Set Use our Breast Cancer Wisconsin Data Set Also you can

Faeze Habibi 0 Apr 6, 2022
Transfer Learning library for Deep Neural Networks.
Transfer Learning library for Deep Neural Networks.

Transfer and meta-learning in Python Each folder in this repository corresponds to a method or tool for transfer/meta-learning. xfer-ml is a standalon

Amazon 245 Dec 8, 2022
Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks Abstract Facial expression recognition in video

Bogireddy Sai Prasanna Teja Reddy 103 Dec 29, 2022
A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

SVHNClassifier-PyTorch A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks If

Potter Hsu 182 Jan 3, 2023
Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors
Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors

-IEEE-TIM-2021-1-Shallow-CNN-for-HAR [IEEE TIM 2021-1] Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors All

Wenbo Huang 1 May 17, 2022
code for our paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

SHOT++ Code for our TPAMI submission "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer" that is ext

null 75 Dec 16, 2022
Transfer style api - An API to use with Tranfer Style App, where you can use two image and transfer the style

Transfer Style API It's an API to use with Tranfer Style App, where you can use

Brian Alejandro 1 Feb 13, 2022
To Design and Implement Logistic Regression to Classify Between Benign and Malignant Cancer Types

To Design and Implement Logistic Regression to Classify Between Benign and Malignant Cancer Types, from a Database Taken From Dr. Wolberg reports his Clinic Cases.

Astitva Veer Garg 1 Jul 31, 2022
Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral)
Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral)

Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral) This is the official implementation of Focals Conv (CVPR 2022), a new sp

DV Lab 280 Jan 7, 2023
Owner
Vipul Shinde
Breaking Bad with #Data 👨‍🔬 | Masters in CS @ University of Georgia | Data Scientist | Machine Learning
Vipul Shinde
GitHub https://www.kaggle.com/c/siim-isic-melanoma-classification
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.

Orientation independent Möbius CNNs This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of

Maurice Weiler 59 Dec 9, 2022
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening Introduction This is an implementation of the model used for breast

null 757 Dec 30, 2022
Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network

DeepCDR Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network This work has been accepted to ECCB2020 and was also published in the

Qiao Liu 50 Dec 18, 2022
Cancer metastasis detection with neural conditional random field (NCRF)

NCRF Prerequisites Data Whole slide images Annotations Patch images Model Training Testing Tissue mask Probability map Tumor localization FROC evaluat

Baidu Research 731 Jan 1, 2023
Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have undergone breast cancer surgery.

Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have undergone breast cancer surgery.

Nafis Ahmed 1 Dec 28, 2021
Improving Convolutional Networks via Attention Transfer (ICLR 2017)

Attention Transfer PyTorch code for "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Tran

Sergey Zagoruyko 1.4k Dec 23, 2022
Learning and Building Convolutional Neural Networks using PyTorch

Image Classification Using Deep Learning Learning and Building Convolutional Neural Networks using PyTorch. Models, selected are based on number of ci

Mayur 126 Dec 22, 2022
Transfer-Learn is an open-source and well-documented library for Transfer Learning.

Transfer-Learn is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms, or readily apply existing algorithms.

THUML @ Tsinghua University 2.2k Jan 3, 2023
Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (MTCNN)

Face-Detection-with-MTCNN Face detection is a computer vision problem that involves finding faces in photos. It is a trivial problem for humans to sol

Chetan Hirapara 3 Oct 7, 2022
Search and filter videos based on objects that appear in them using convolutional neural networks

Thingscoop: Utility for searching and filtering videos based on their content Description Thingscoop is a command-line utility for analyzing videos se

Anastasis Germanidis 354 Dec 4, 2022