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Deep Melanoma classification with K-Fold Cross-Validation for Process optimization
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
University of Salerno, Dept. of Industrial Engineering, Salerno, Italy.
University of Salerno, Dept. of Industrial Engineering, Salerno, Italy.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
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2020 (English)In: 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), IEEE, 2020Conference paper, Published paper (Refereed)
Abstract [en]

Deep convolution neural networks (DCNNs) enable effective methods to predict the melanoma classes otherwise found with ultrasonic extraction. However, gathering large datasets in local hospitals in Sweden can take years. Small datasets will result in models with poor accuracy and insufficient generalization ability, which has a great impact on the result. This paper proposes to use a K-Fold cross validation approach based on a DCNN algorithm working on a small sample dataset. The performance of the model is verified via a Vgg16 extracting the features. The experimental results reveal that the model built by the approach proposed in this paper can effectively achieve a better prediction and enhance the accuracy of the model, which proves that K-Fold can achieve better performance on a small skin cancer dataset. 

Place, publisher, year, edition, pages
IEEE, 2020.
Keywords [en]
classification, DCNNs, K-Fold, melanoma
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:miun:diva-39610DOI: 10.1109/MeMeA49120.2020.9137222ISI: 000612835700073Scopus ID: 2-s2.0-85088904068ISBN: 978-1-7281-5386-5 (electronic)OAI: oai:DiVA.org:miun-39610DiVA, id: diva2:1458663
Conference
2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)
Available from: 2020-08-17 Created: 2020-08-17 Last updated: 2023-01-20Bibliographically approved
In thesis
1. Automatic Melanoma Diagnosis in Dermoscopic Imaging Base on Deep Learning System
Open this publication in new window or tab >>Automatic Melanoma Diagnosis in Dermoscopic Imaging Base on Deep Learning System
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Melanoma is one of the deadliest forms of cancer. Unfortunately, its incidence rates have been increasing all over the world. One of the techniques used by dermatologists to diagnose melanomas is an imaging modality called dermoscopy. The skin lesion is inspected using a magnification device and a light source. This technique makes it possible for the dermatologist to observe subcutaneous structures that would be invisible otherwise. However, the use of dermoscopy is not straightforward, requiring years of practice. Moreover, the diagnosis is many times subjective and challenging to reproduce. Therefore, it is necessary to develop automatic methods that will help dermatologists provide more reliable diagnoses. 

Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. Recent developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in the clinical diagnostic ability to the point that it can detect melanoma in the clinic at the earliest stages. This technology’s global adoption has allowed the accumulation of extensive collections of dermoscopy images. The development of advanced technologies in image processing and machine learning has given us the ability to distinguish malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow earlier detection of melanoma and reduce a large number of unnecessary and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, a widespread implementation must await further technical progress in accuracy and reproducibility. 

This thesis provides an overview of our deep learning (DL) based methods used in the diagnosis of melanoma in dermoscopy images. First, we introduce the background. Then, this paper gives a brief overview of the state-of-art article on melanoma interpret. After that, a review is provided on the deep learning models for melanoma image analysis and the main popular techniques to improve the diagnose performance. We also made a summary of our research results. Finally, we discuss the challenges and opportunities for automating melanocytic skin lesions’ diagnostic procedures. We end with an overview of a conclusion and directions for the following research plan. 

Place, publisher, year, edition, pages
Mid Sweden University, 2021. p. 32
Series
Mid Sweden University licentiate thesis, ISSN 1652-8948 ; 180
Keywords
Melanoma classification, computer vision, Deep learning, CNN
National Category
Dermatology and Venereal Diseases Medical Imaging Computer Engineering
Identifiers
urn:nbn:se:miun:diva-41751 (URN)978-91-89341-00-5 (ISBN)
Presentation
2021-04-23, C312, Holmgatan 10, Sundsvall, 13:00 (English)
Opponent
Supervisors
Available from: 2021-03-29 Created: 2021-03-26 Last updated: 2025-02-09Bibliographically approved
2. 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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