Modified U-NET Architecture for Segmentation of Skin Lesion

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2022 Feb 15

PMID 35161613

Authors

Vatsala Anand

Sheifali Gupta

Deepika Koundal

Soumya Ranjan Nayak

Paolo Barsocchi

Akash Kumar Bhoi

Affiliations

Soon will be listed here.

Abstract

Dermoscopy images can be classified more accurately if skin lesions or nodules are segmented. Because of their fuzzy borders, irregular boundaries, inter- and intra-class variances, and so on, nodule segmentation is a difficult task. For the segmentation of skin lesions from dermoscopic pictures, several algorithms have been developed. However, their accuracy lags well behind the industry standard. In this paper, a modified U-Net architecture is proposed by modifying the feature map's dimension for an accurate and automatic segmentation of dermoscopic images. Apart from this, more kernels to the feature map allowed for a more precise extraction of the nodule. We evaluated the effectiveness of the proposed model by considering several hyper parameters such as epochs, batch size, and the types of optimizers, testing it with augmentation techniques implemented to enhance the amount of photos available in the PH2 dataset. The best performance achieved by the proposed model is with an Adam optimizer using a batch size of 8 and 75 epochs.

Citing Articles

Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model.

Ahmed A, Sun G, Bilal A, Li Y, Ebad S Sci Rep. 2025; 15(1):4815.

PMID: 39924555 PMC: 11808120. DOI: 10.1038/s41598-025-88753-3.

An Enhanced Approach Using AGS Network for Skin Cancer Classification.

Lee H, Cho S, Song J, Kim H, Shin Y Sensors (Basel). 2025; 25(2).

PMID: 39860766 PMC: 11769443. DOI: 10.3390/s25020394.

Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images.

Sikder N, Khan M, Bairagi A, Masud M, Tiang J, Nahid A Sci Rep. 2024; 14(1):28954.

PMID: 39578636 PMC: 11584783. DOI: 10.1038/s41598-024-80013-0.

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.

Xu Y, Quan R, Xu W, Huang Y, Chen X, Liu F Bioengineering (Basel). 2024; 11(10).

PMID: 39451409 PMC: 11505408. DOI: 10.3390/bioengineering11101034.

EAAC-Net: An Efficient Adaptive Attention and Convolution Fusion Network for Skin Lesion Segmentation.

Fan C, Zhu Z, Peng B, Xuan Z, Zhu X J Imaging Inform Med. 2024; .

PMID: 39147886 DOI: 10.1007/s10278-024-01223-6.

References

She Z, Liu Y, Damatoa A . Combination of features from skin pattern and ABCD analysis for lesion classification. Skin Res Technol. 2007; 13(1):25-33. DOI: 10.1111/j.1600-0846.2007.00181.x. View

Liu L, Mou L, Zhu X, Mandal M . Automatic skin lesion classification based on mid-level feature learning. Comput Med Imaging Graph. 2020; 84:101765. DOI: 10.1016/j.compmedimag.2020.101765. View

Gupta K, Roy S, Poonia R, Nayak S, Kumar R, Alzahrani K . Evaluating the Usability of mHealth Applications on Type 2 Diabetes Mellitus Using Various MCDM Methods. Healthcare (Basel). 2022; 10(1). PMC: 8775296. DOI: 10.3390/healthcare10010004. View

Yuan Y, Chao M, Lo Y . Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance. IEEE Trans Med Imaging. 2017; 36(9):1876-1886. DOI: 10.1109/TMI.2017.2695227. View

Ibtehaz N, Sohel Rahman M . MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 2019; 121:74-87. DOI: 10.1016/j.neunet.2019.08.025. View

Al-Masni M, Kim D, Kim T . Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Comput Methods Programs Biomed. 2020; 190:105351. DOI: 10.1016/j.cmpb.2020.105351. View

Yu Z, Jiang X, Zhou F, Qin J, Ni D, Chen S . Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features. IEEE Trans Biomed Eng. 2018; 66(4):1006-1016. DOI: 10.1109/TBME.2018.2866166. View

Garg M, Gupta S, Nayak S, Nayak J, Pelusi D . Modified pixel level snake using bottom hat transformation for evolution of retinal vasculature map. Math Biosci Eng. 2021; 18(5):5737-5757. DOI: 10.3934/mbe.2021290. View

Garnavi R, Aldeen M, Celebi M, Varigos G, Finch S . Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Comput Med Imaging Graph. 2010; 35(2):105-15. DOI: 10.1016/j.compmedimag.2010.08.001. View

10.

Xie Y, Zhang J, Xia Y, Shen C . A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification. IEEE Trans Med Imaging. 2020; 39(7):2482-2493. DOI: 10.1109/TMI.2020.2972964. View

11.

GANSTER H, Pinz A, ROHRER R, Wildling E, Binder M, Kittler H . Automated melanoma recognition. IEEE Trans Med Imaging. 2001; 20(3):233-9. DOI: 10.1109/42.918473. View

12.

Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille A . DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans Pattern Anal Mach Intell. 2017; 40(4):834-848. DOI: 10.1109/TPAMI.2017.2699184. View

13.

Novikov A, Lenis D, Major D, Hladuvka J, Wimmer M, Buhler K . Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs. IEEE Trans Med Imaging. 2018; 37(8):1865-1876. DOI: 10.1109/TMI.2018.2806086. View

14.

Schaefer G, Rajab M, Celebi M, Iyatomi H . Colour and contrast enhancement for improved skin lesion segmentation. Comput Med Imaging Graph. 2010; 35(2):99-104. DOI: 10.1016/j.compmedimag.2010.08.004. View

15.

Mishra S, Tripathy H, Mallick P, Bhoi A, Barsocchi P . EAGA-MLP-An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis. Sensors (Basel). 2020; 20(14). PMC: 7411768. DOI: 10.3390/s20144036. View

16.

Li Y, Shen L . Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network. Sensors (Basel). 2018; 18(2). PMC: 5855504. DOI: 10.3390/s18020556. View

17.

Liu L, Tsui Y, Mandal M . Skin Lesion Segmentation Using Deep Learning with Auxiliary Task. J Imaging. 2021; 7(4). PMC: 8321325. DOI: 10.3390/jimaging7040067. View

18.

Bi L, Kim J, Ahn E, Kumar A, Fulham M, Feng D . Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks. IEEE Trans Biomed Eng. 2017; 64(9):2065-2074. DOI: 10.1109/TBME.2017.2712771. View

19.

Li H, He X, Zhou F, Yu Z, Ni D, Chen S . Dense Deconvolutional Network for Skin Lesion Segmentation. IEEE J Biomed Health Inform. 2018; 23(2):527-537. DOI: 10.1109/JBHI.2018.2859898. View

20.

Erkol B, Moss R, Stanley R, V Stoecker W, Hvatum E . Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes. Skin Res Technol. 2005; 11(1):17-26. PMC: 3184888. DOI: 10.1111/j.1600-0846.2005.00092.x. View