Review of medical image recognition technologies to detect melanomas using neural networks

Abstract

Background Melanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization estimates, 132,000 cases of the disease and 66,000 deaths from malignant melanoma and other forms of skin cancer are reported annually worldwide (https://apps.who.int/gho/data/?theme=main) and those numbers continue to grow. In our opinion, due to the increasing incidence of the disease, it is necessary to find new, easy to use and sensitive methods for the early diagnosis of melanoma in a large number of people around the world. Over the last decade, neural networks show highly sensitive, specific, and accurate results. Objective This study presents a review of PubMed papers including requests «melanoma neural network» and «melanoma neural network dermatoscopy». We review recent researches and discuss their opportunities acceptable in clinical practice. Methods We searched the PubMed database for systematic reviews and original research papers on the requests «melanoma neural network» and «melanoma neural network dermatoscopy» published in English. Only papers that reported results, progress and outcomes are included in this review. Results We found 11 papers that match our requests that observed convolutional and deep-learning neural networks combined with fuzzy clustering or World Cup Optimization algorithms in analyzing dermatoscopic images. All of them require an ABCD (asymmetry, border, color, and differential structures) algorithm and its derivates (in combination with ABCD algorithm or separately). Also, they require a large dataset of dermatoscopic images and optimized estimation parameters to provide high specificity, accuracy and sensitivity. Conclusions According to the analyzed papers, neural networks show higher specificity, accuracy and sensitivity than dermatologists. Neural networks are able to evaluate features that might be unavailable to the naked human eye. Despite that, we need more datasets to confirm those statements. Nowadays machine learning becomes a helpful tool in early diagnosing skin diseases, especially melanoma.

3D-Printed Collagen Scaffolds Promote Maintenance of Cryopreserved Patients-Derived Melanoma Explants

“…There remain unresolved questions about the biological significance of 3D-printed collagen scaffolds embedded in the melanoma behavior after transplantation. Moreover, it remains controversial whether all cells derived from cryopreserved PDME maintain their original condition and accurately depict the microenvironment of melanoma in patients [ 1 , 2 , 5 , 27 ]. Additional studies based on collagen-mediated melanoma behavior in mice and in patients should be able to provide greater clarity [ 27 , 28 ].…”

“…Melanoma is a highly aggressive skin cancer. It can grow deep into the skin layer and spread to other parts of the body very rapidly if it is not treated at an early stage [ 1 , 2 ]. Melanoma therapy choices depend on the stage of the cancer, the location of the tumor, and the patient’s health.…”

“…Melanoma therapy choices depend on the stage of the cancer, the location of the tumor, and the patient’s health. Melanoma that is discovered at an early stage is detected in the upper layers of the skin and without any indication of further spreading [ 1 , 2 ]. Early-stage melanoma can be removed easily via surgery.…”

Maize Diseases Identification Method Based on Multi-Scale Convolutional Global Pooling Neural Network

“…In the past few years, as a special type of machine learning, deep learning methods have made great progress. Nowadays, the deep learning methods have been applied in various fields [15][16][17][18][19][20][21]. As Dawei et al [20] proposed a pest identification and detection system based on transfer learning, the recognition accuracy can reach 93.84%.…”

Perceptions of virtual primary care physicians: A focus group study of medical and data science graduate students

“…Recently, technology has also markedly contributed to the field of radiology. For example, artificial intelligence has recently been shown to be more accurate than radiologists in breast cancer prediction [ 17 ], and to have similar or better accuracy than dermatologists in diagnosis of melanoma [ 18 , 19 ].…”