Summary of Skin Cancer Diagnosis Using Nir Spectroscopy Data Of Skin Lesions in Vivo Using Machine Learning Algorithms, by Flavio P. Loss et al.

Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms

by Flavio P. Loss, Pedro H. da Cunha, Matheus B. Rocha, Madson Poltronieri Zanoni, Leandro M. de Lima, Isadora Tavares Nascimento, Isabella Rezende, Tania R. P. Canuto, Luciana de Paula Vieira, Renan Rossoni, Maria C. S. Santos, Patricia Lyra Frasson, Wanderson Romão, Paulo R. Filgueiras, Renato A. Krohling

First submitted to arxiv on: 2 Jan 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The abstract discusses the development of a computer-aided diagnostic (CAD) system for early diagnosis of skin cancer, specifically melanoma. The authors explore the limitations of traditional CAD approaches using image and clinical data, highlighting the need for an alternative source of information that can provide insights into the molecular structure of the lesion. They propose using Near-Infrared (NIR) spectroscopy to classify skin lesions as benign or malignant. The paper presents various machine learning algorithms, including XGBoost, CatBoost, LightGBM, and 1D-convolutional neural network (1D-CNN), which are applied to classify cancerous and non-cancerous skin lesions. The results indicate that LightGBM with pre-processing using standard normal variate (SNV) achieves the best performance.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper is about developing a computer system to help doctors diagnose skin cancer, especially melanoma, earlier. Traditionally, doctors use images and patient information to make a diagnosis. However, this approach has limitations because it doesn’t provide information about the molecular structure of the lesion. The authors suggest using a new technology called Near-Infrared (NIR) spectroscopy to help diagnose skin cancer. They test various computer algorithms to see which one works best for classifying skin lesions as benign or malignant. The results show that one algorithm, LightGBM, is particularly effective in making accurate diagnoses.