Skin Cancer Classification
14 papers with code • 1 benchmarks • 1 datasets
ISIC 2020 Challenge Dataset
Latest papers with no code
New pyramidal hybrid textural and deep features based automatic skin cancer classification model: Ensemble DarkNet and textural feature extractor
Hence, a machine learning-based automatic skin cancer detection model must be developed.
Skin Cancer Classification using Inception Network and Transfer Learning
Medical data classification is typically a challenging task due to imbalance between classes.
Benchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset
Later, these images were trained with transfer learning and fine-tuning approach and deep learning models were created in this way.
A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information
In this work, we present a smartphone-based application to assist on skin cancer detection.
Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets
We propose a novel ensemble-based CNN architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with auxiliary data in the form of metadata associated with the input images, are combined using a meta-learner.
Transfer learning with class-weighted and focal loss function for automatic skin cancer classification
To aid dermatologists in skin cancer diagnosis, we developed a deep learning system that can effectively and automatically classify skin lesions into one of the seven classes: (1) Actinic Keratoses, (2) Basal Cell Carcinoma, (3) Benign Keratosis, (4) Dermatofibroma, (5) Melanocytic nevi, (6) Melanoma, (7) Vascular Skin Lesion.
Convolutional Neural Networks for Classifying Melanoma Images
In this work, we address the problem of skin cancer classification using convolutional neural networks.
Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages
The experimental results demonstrate notable improvement in train and validation accuracy by using the refined version of images of both the networks, however, the Inception-v3 network was able to achieve better validation accuracy thus it was finally selected to evaluate it on test data.
Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers
Technology aided platforms provide reliable tools in almost every field these days.
Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets
We find that the majority of the data in the the two datasets have ITA values between 34. 5{\deg} and 48{\deg}, which are associated with lighter skin, and is consistent with under-representation of darker skinned populations in these datasets.
