Skin Lesion Analysis for Melanoma Detection Using the Novel Deep Learning Model Fuzzy GC-SCNN

Overview

Journal Healthcare (Basel)

Specialty Health Services

Date 2022 May 28

PMID 35628098

Authors

Usharani Bhimavarapu

Gopi Battineni

Affiliations

Soon will be listed here.

Abstract

Melanoma is easily detectable by visual examination since it occurs on the skin's surface. In melanomas, which are the most severe types of skin cancer, the cells that make melanin are affected. However, the lack of expert opinion increases the processing time and cost of computer-aided skin cancer detection. As such, we aimed to incorporate deep learning algorithms to conduct automatic melanoma detection from dermoscopic images. The fuzzy-based GrabCut-stacked convolutional neural networks (GC-SCNN) model was applied for image training. The image features extraction and lesion classification were performed on different publicly available datasets. The fuzzy GC-SCNN coupled with the support vector machines (SVM) produced 99.75% classification accuracy and 100% sensitivity and specificity, respectively. Additionally, model performance was compared with existing techniques and outcomes suggesting the proposed model could detect and classify the lesion segments with higher accuracy and lower processing time than other techniques.

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References

Sivaraj S, Malmathanraj R, Palanisamy P . Detecting anomalous growth of skin lesion using threshold-based segmentation algorithm and Fuzzy K-Nearest Neighbor classifier. J Cancer Res Ther. 2020; 16(1):40-52. DOI: 10.4103/jcrt.JCRT_306_17. View

Pacheco A, Krohling R . The impact of patient clinical information on automated skin cancer detection. Comput Biol Med. 2019; 116:103545. DOI: 10.1016/j.compbiomed.2019.103545. View

Nasir M, Khan M, Sharif M, Lali I, Saba T, Iqbal T . An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach. Microsc Res Tech. 2018; 81(6):528-543. DOI: 10.1002/jemt.23009. View

Esteva A, Kuprel B, Novoa R, Ko J, Swetter S, Blau H . Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542(7639):115-118. PMC: 8382232. DOI: 10.1038/nature21056. View

Khan M, Sharif M, Akram T, Damasevicius R, Maskeliunas R . Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization. Diagnostics (Basel). 2021; 11(5). PMC: 8145295. DOI: 10.3390/diagnostics11050811. View

Cai G, Zhu Y, Wu Y, Jiang X, Ye J, Yang D . A multimodal transformer to fuse images and metadata for skin disease classification. Vis Comput. 2022; :1-13. PMC: 9070977. DOI: 10.1007/s00371-022-02492-4. View

Huang H, Hsu B, Lee C, Tseng V . Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers. J Dermatol. 2020; 48(3):310-316. DOI: 10.1111/1346-8138.15683. View

R D S, A S . Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM). Asian Pac J Cancer Prev. 2019; 20(5):1555-1561. PMC: 6857898. DOI: 10.31557/APJCP.2019.20.5.1555. View

Saba T, Khan M, Rehman A, Marie-Sainte S . Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction. J Med Syst. 2019; 43(9):289. DOI: 10.1007/s10916-019-1413-3. View

10.

Sabbaghi Mahmouei S, Aldeen M, V Stoecker W, Garnavi R . Biologically Inspired QuadTree Color Detection in Dermoscopy Images of Melanoma. IEEE J Biomed Health Inform. 2018; 23(2):570-577. DOI: 10.1109/JBHI.2018.2841428. View

11.

Brinker T, Hekler A, Utikal J, Grabe N, Schadendorf D, Klode J . Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review. J Med Internet Res. 2018; 20(10):e11936. PMC: 6231861. DOI: 10.2196/11936. View

12.

Liu Q, Yu L, Luo L, Dou Q, Heng P . Semi-Supervised Medical Image Classification With Relation-Driven Self-Ensembling Model. IEEE Trans Med Imaging. 2020; 39(11):3429-3440. DOI: 10.1109/TMI.2020.2995518. View

13.

Hekler A, Utikal J, Enk A, Hauschild A, Weichenthal M, Maron R . Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer. 2019; 120:114-121. DOI: 10.1016/j.ejca.2019.07.019. View

14.

Tschandl P, Rosendahl C, Akay B, Argenziano G, Blum A, Braun R . Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks. JAMA Dermatol. 2018; 155(1):58-65. PMC: 6439580. DOI: 10.1001/jamadermatol.2018.4378. View

15.

Sun Q, Huang C, Chen M, Xu H, Yang Y . Skin Lesion Classification Using Additional Patient Information. Biomed Res Int. 2021; 2021:6673852. PMC: 8055397. DOI: 10.1155/2021/6673852. View

16.

Afza F, Khan M, Sharif M, Rehman A . Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection. Microsc Res Tech. 2019; 82(9):1471-1488. DOI: 10.1002/jemt.23301. View

17.

Gessert N, Nielsen M, Shaikh M, Werner R, Schlaefer A . Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data. MethodsX. 2020; 7:100864. PMC: 7150512. DOI: 10.1016/j.mex.2020.100864. View

18.

Iqbal I, Younus M, Walayat K, Ullah Kakar M, Ma J . Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images. Comput Med Imaging Graph. 2021; 88:101843. DOI: 10.1016/j.compmedimag.2020.101843. View

19.

Rogers H, Weinstock M, Feldman S, Coldiron B . Incidence Estimate of Nonmelanoma Skin Cancer (Keratinocyte Carcinomas) in the U.S. Population, 2012. JAMA Dermatol. 2015; 151(10):1081-6. DOI: 10.1001/jamadermatol.2015.1187. View

20.

Khan M, Muhammad K, Sharif M, Akram T, de Albuquerque V . Multi-Class Skin Lesion Detection and Classification via Teledermatology. IEEE J Biomed Health Inform. 2021; 25(12):4267-4275. DOI: 10.1109/JBHI.2021.3067789. View