DiscountManager API client

Developing a CNN-based model to diagnose skin cancer using the ISIC-2019 dataset.

skin cancer detection model

The aim of this study is to develop a deep learning model using CNNs for accurate skin cancer diagnosis from the ISIC-2019 dataset and to optimize hyperparameters using differential evolution algorithms.

Multimodal melanoma classifier trained on harmonized ISIC 2024 + iToBoS 3D data; metadata-aware pipeline with domain adaptation and cross-dataset evaluation.

U-Net & ConvLSTM melanoma segmentation with SLIC superpixels, mask-to-bounding-box ROI cropping, and cross-ISIC (2016–2020) evaluation; ISIC-2017 results: U-Net 86.43% acc / 55.17% IoU, ConvLSTM 85% acc / 48% IoU.

ISIC challenge leaderboard Analytics Tool

Research-driven multi-stage ensemble pipeline for skin cancer detection using deep learning.

This project provides a solution for skin cancer classification using Convolutional Neural Networks (CNN) and Transfer Learning techniques with TensorFlow and Keras. It includes instructions for installation, dataset acquisition, and usage through Jupyter notebooks .

Assignments and Projects of CO410 AI and Expert Systems Course at NITK Surathkal

This project combines traditional machine learning approaches with advanced deep learning techniques to assist healthcare professionals in early diagnosis and improve patient outcomes.

Repository for the paper: "Domain Adaptation for Skin Lesion: Evaluating Real-World Generalisation"

Parses the "Classification of Economic Activities" (wz2008) issued by the Statistisches Bundesamt to build multiple hierarchically structured trees.

Source code for the paper: "Dermoscopic Dark Corner Artifacts Removal: Friend or Foe?"

This project aims to predict the presence of skin cancer using a hybrid deep learning model that integrates both tabular data and image data. The dataset used is the ISIC dataset, which contains skin lesion images along with associated metadata. The model achieves an accuracy of 88% on the test set.

ISIC Archive API v2 download images by ISIC ID

Deep Learning based Skin Cancer Detection using multiple CNN architectures (VGG, ResNet, DenseNet, EfficientNet, Inception) with image preprocessing using ESRGAN and performance comparison for clinical AI research.

Source code for the paper: "Selective Alignment Transfer for Domain Adaptation in Skin Lesion Analysis".

Quickest product classification according to industry standards (Web and API)

Repository for the paper: "Skin Lesion Classification Using Dermoscopic Images and Clinical Metadata: Insights from Multimodal Models"