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A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).
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2023 (English)In: Diagnostics, ISSN 2075-4418, Vol. 13, no 1, article id 72Article in journal (Refereed) Published
Abstract [en]

Skin cancers are the most cancers diagnosed worldwide, with an estimated > 1.5 million new cases in 2020. Use of computer-aided diagnosis (CAD) systems for early detection and classification of skin lesions helps reduce skin cancer mortality rates. Inspired by the success of the transformer network in natural language processing (NLP) and the deep convolutional neural network (DCNN) in computer vision, we propose an end-to-end CNN transformer hybrid model with a focal loss (FL) function to classify skin lesion images. First, the CNN extracts low-level, local feature maps from the dermoscopic images. In the second stage, the vision transformer (ViT) globally models these features, then extracts abstract and high-level semantic information, and finally sends this to the multi-layer perceptron (MLP) head for classification. Based on an evaluation of three different loss functions, the FL-based algorithm is aimed to improve the extreme class imbalance that exists in the International Skin Imaging Collaboration (ISIC) 2018 dataset. The experimental analysis demonstrates that impressive results of skin lesion classification are achieved by employing the hybrid model and FL strategy, which shows significantly high performance and outperforms the existing work. 

Place, publisher, year, edition, pages
2023. Vol. 13, no 1, article id 72
Keywords [en]
deep learning, focal loss, hybrid model, skin lesion
National Category
Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:miun:diva-46860DOI: 10.3390/diagnostics13010072ISI: 000908965800001Scopus ID: 2-s2.0-85145859869OAI: oai:DiVA.org:miun-46860DiVA, id: diva2:1727727
Available from: 2023-01-17 Created: 2023-01-17 Last updated: 2025-02-07Bibliographically approved
In thesis
1. Deep Learning Approaches towards Skin Lesion Classification with Dermoscopic Images
Open this publication in new window or tab >>Deep Learning Approaches towards Skin Lesion Classification with Dermoscopic Images
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Melanoma is a skin cancer that tends to be deadly. The incidence of melanoma is currently at the highest level ever recorded in Europe, North America and Oceania. The survival rate can be significantly increased if skin lesions are identified in dermoscopic images at an early stage. In the other hand, the classification of skin lesions is incredibly challenging. Skin lesion classification using deep learning approaches has provided better results in classifying skin diseases than those of dermatologist, which is lifesaving in terms of diagnosis.

This thesis presents a review of our research articles on classifying skin lesions using deep learning. Regarding the research, I have four goals concerning research frontier work, small datasets, data imbalance, and improving accuracy. In this thesis, I discuss how deep learning can classify skin diseases, summarizing the problems that remain at this stage and the outlook for the future.

For the above goals, I first studied and summarized more than 200 highguality articles published over five years. I then used three versions of You only look once (Yolo) to detect skin lesions. Although there were only 200 pictures, the test was very effective for detection. I applied the five-fold algorithm to Vgg_16, trained five models, and fused them so solve the small data problem. To improve the accuracy, I also tried to combine the traditional machine learning method, i.e., the seven-point checklist, with three different backbones. Since the learning rate. Then, I also tried to use the hybrid model, combining convolutional neural networks (CNN) and transformer to train the dataset, and applied focal loss to balance the extremely unbalanced weight of the data.

In addition to high-quality data sets and high-performance computers being extremely important in the research and application of deep learning, the optimization of machine learning algorithms for skin lesions can be endless

Abstract [sv]

Melanom är en form av hudcancer som tenderar att vara dödlig. Förekomsten av melanom är för närvarande på den högsta nivån som någonsin registrerats i Europa, Nordamerika och Oceanien. Chansen för överlevnad ökar avsevärt om hudskadorna identifieras i dermatoskopiska bilder i ett tidigare skede, men klassificering av hudskador är otroligt utmanande. Med metoder för djupinlärning har klassificering av hudsjukdomar i vissa fall gett bättre resultat än hudläkares diagnoser, vilket ger större möjligheter att rädda liv.

Denna avhandling presenterar en genomgång av våra forskningsartiklar om klassificering av hudskador med hjälp av djupinlärning. När det gäller vår forskning har jag fyra mål som handlar om forskningens frontlinjearbete, små datamängder, obalans i data och om att förbättra noggrannheten. I detta avhandlingsarbete diskuterar jag hur djupinlärning kan klassificera hudsjukdomar, sammanfattar de problem som kvarstår i detta skede och diskuterar utsikterna för framtiden.

För ovanstående mål studerade och sammanfattade jag först mer än 200 högkvalitativa artiklar publicerade under fem år. Jag använde sedan tre versioner av You only look once (Yolo) för att upptäcka hudskador. Även om det bara fanns 200 bilder var testet mycket effektivt för upptäckt. Jag tillämpade en femdelad algoritm på Vhh-16, tränade fem modeller och sammanfogade dem för att lösa problemet med små datamängder. För att förbättra noggrannheten försökte jag också kombinera en sjupunkts checklista, förstärkt med maskininlärning, med tre olika grundstommar. Eftersom inlärningshastigheten starkt påverkar modellträningen använde jag cosinus-inlärningshastigheten. Sedan försökte jag också använda hybridmodellen, som kombinerade konvolutionella neurala nätverk (CNN) och transformator för att träna dataset, och tillämpade fokalförlust för att balansera den extremt obalanserade vikten av datan.

Förutom att högkvalitativa datamängder och högpresterande datorer är extremt viktiga i forskningen och tillämpningen av djupinlärning, kan optimeringen av maskininlärningsalgoritmer för hudskador vara oändliga.

Place, publisher, year, edition, pages
Sundsvall: Mid Sweden University, 2023. p. 51
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 383
National Category
Medical Imaging
Identifiers
urn:nbn:se:miun:diva-46957 (URN)978-91-89341-86-9 (ISBN)
Public defence
2023-02-16, C312, Holmgatan 10, Sundsvall, 09:00 (English)
Supervisors
Available from: 2023-01-20 Created: 2023-01-19 Last updated: 2025-02-09Bibliographically approved

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Nie, YaliO'Nils, MattiasLundgren, Jan

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