# Skin Cancer Classification using Inception Network and Transfer Learning

arXiv: https://arxiv.org/abs/2111.02402 · HTML: https://mlsanity.com/paper/2111.02402/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [The Marginal Value of Adaptive Gradient Methods in Machine Learning](https://mlsanity.com/paper/1705.08292/) (2017)

## Built on by (most cited first, 2 of 2)

- [Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-based Non-invasive Digital System](https://mlsanity.com/paper/2401.04746/) (2024)
- [An example of use of Variational Methods in Quantum Machine Learning](https://mlsanity.com/paper/2208.04316/) (2022)

## Abstract (arXiv)

Medical data classification is typically a challenging task due to imbalance between classes. In this paper, we propose an approach to classify dermatoscopic images from HAM10000 (Human Against Machine with 10000 training images) dataset, consisting of seven imbalanced types of skin lesions, with good precision and low resources requirements. Classification is done by using a pretrained convolutional neural network. We evaluate the accuracy and performance of the proposal and illustrate possible extensions.