Volume 13 , Issue 2 , PP: 155-170, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Arpita Roy 1 , Shaik Razia 2 *
Doi: https://doi.org/10.54216/JCIM.130212
Skin cancer has become more common in recent decades, raising severe concerns about world health. Creating an automated system to distinguish between benign and malignant images is challenging because of the subtle variations in how skin lesions appear. This study introduces Computer-Aided Diagnosis (CAD) system that offers high classification accuracy while maintaining low computing complexity for categorizing skin lesions. The system incorporates a pre-processing stage that uses morphological filtering to remove hair and artefacts. With the least minimum of human interaction, deep learning techniques are employed to separate skin lesions automatically. Image processing methods are currently being utilized to investigate the automated implementation of the prediction criteria for distinguishing between benign and malignant melanoma lesions. Various pre-trained convolutional neural networks (CNNs) with multi-layered (ML-CNN) are under examination for the classification of skin lesions as either benign or malignant. The best performance is achieved when RF, k-NN and XGBoost are combined, according to average 5-fold cross-validation findings. The outcomes also demonstrate that data augmentation works better than acquiring novel images for training and testing purposes. The experiment results show that the suggested diagnostic framework performs better than existing methods when used on actual clinical skin lesions, with accuracy at 97.5%, F1-score at 91.3%, precision at 96.5%, sensitivity at 89.2% and specificity at 96.7%. It also takes 2.6 seconds to complete with the MNIST dataset and accuracy at 98.2%, F1-score at 92.5%, precision at 98.4%, sensitivity at 92.3% and specificity of 97.2% with the ISIC dataset. This indicates that medical professionals can benefit from using the suggested framework to classify various skin lesions.
skin lesions , prediction , accuracy , deep learning , malignant image
[1] Yang Y, Wang W, Yin Z, Xu R, Zhou X, Kumar N, Alazab M, Gadekallu TR. Mixed game-based AI optimization for combating COVID-19 with AI bots. IEEE J Sel Areas Commun. 2022;40(11):3122–38.
[2] Hwang SM, Pan HC, Hwang MK, Kim MW, Lee JS. Malignant skin tumours are misdiagnosed as benign skin lesions. Arch Craniofac Surg. 2016;17(2):86–9
[3] Eltoukhy MM, Hosny KM, Kassem MA. Classification of multiclass histopathological breast images using residual deep learning. Comput Intell Neurosci. 2022;10(2022):9086060
[4] Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017;21(1):4–21.
[5] Catarina B, Emre Celebi M, Marques JS. Development of a clinically oriented system for melanoma diagnosis. Pattern Recogn. 2017;69:270–85
[6] Zortea M, Flores K, Scharcanski J. A simple weighted thresholding method for segmenting pigmented skin lesions in macroscopic images. Pattern Recogn. 2017;64(8):92–104.
[7] Pandya S, Gadekallu TR, Reddy PK, Wang W, Alazab M. InfusedHeart: a novel knowledge-infused learning framework for diagnosis of cardiovascular events. IEEE Trans Comput Soc Syst. 2022.
[8] Hu Z, Tang J, Wang Z, Zhang K, Sun Q. Deep learning for image-based cancer detection and diagnosis—a survey. Pattern Recogn. 2018;83:134–49.
[9] Bi L, Kim J, Ahn E, Kumar A, Dagan F, Fulham M. Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recogn. 2019;85:78–89
[10] Hosny KM, Kassem MA. Refined residual deep convolutional network for skin lesion classification. J Digit Imaging. 2022;35(2):258–80.
[11] Ozkan IA, Koklu M. Skin lesion classification using machine learning algorithms. Int J Intell Syst Appl Eng. 2017;5(4):285–9.
[12] Singh R, Bharti V, Purohit V, Kumar A, Singh AK, Singh SK. MetaMed: few-shot medical image classification using gradient-based meta-learning. Pattern Recognit. 2021;120:108111.
[13] Fu Z, An J, Yang Q, Yuan H, Sun Y, Ebrahimian H. Skin cancer detection using Kernel Fuzzy C-means and developed red fox optimization algorithm. Biomed Signal Proc Cont. 2022;71:103160
[14] Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8
[15] Yu L, Chen H, Dou Q, Qin J, Heng PA. Automated melanoma recognition in dermoscopy images via intense residual networks. IEEE Trans Med Imaging. 2017;36(4):994–1004.
[16] Amin J, Sharif A, Gul N, Anjum MA, Nisar MW, Azam F, Bukhari SA. C, Integrated design of deep features fusion for skin cancer localization and classification. Pattern Recogn Lett. 2020;131:63–70.
[17] Mahbod A, Schaefer G, Ellinger I, Ecker R, Pitiot A, Wang C. Fusing fine-tuned deep features for skin lesion classification. Comput Med Imaging Graph. 2019;71:19–29
[18] Hosny KM, Kassem MA, Foaud MM. Classification of skin lesions using transfer learning and augmentation with Alexie. PLoS ONE. 2019;14(5):e0217293
[19] Hosny KM, Kassem MA, Fouad MM. Classification of skin lesions into seven classes using transfer learning with Alexie. J Digit Imaging. 2020;33(5):1325–34
[20] Hosny KM, Kassem MA, Foaud MM. Skin melanoma classification using ROI and data augmentation with deep convolutional neural networks. Multimedia Tools Appl. 2020;79(33):24029–55.
[21] Yu Z, Jiang X, Zhou F, Qin J, Ni D, Chen S, Lei B, Wang T. Melanoma recognition in dermoscopy images via aggregated deep convolutional features. IEEE Trans Biomed Eng. 2019;66(4):1006–16
[22] Majtner T, Yildirim-Yayilgan S, Hardeberg JY. Optimized deep learning features for improved melanoma detection. Multimedia Tools Appl. 2019;78:11883–903.
[23] Albert BA. Deep learning from limited training data: novel segmentation and ensemble algorithms applied to automatic melanoma diagnosis. IEEE Access. 2020;8:31254–69
[24] Harangi B, Baran A, Hajdu A. Assisted deep learning framework for multiclass skin lesion classification considering a binary classification support. Biomed Signal Proc Cont. 2020;62:102041.
[25] Benyahia S, Meftah B, Lézoray O. Multi-features extraction based on deep learning for skin lesion classification. Tissue Cell. 2022;74:101701.
[26] Sathya Preiya V, Kumar VDA. Deep Learning-Based Classification and Feature Extraction for Predicting Pathogenesis of Foot Ulcers in Patients with Diabetes. Diagnostics. 2023; 13(12):1983. https://doi.org/10.3390/diagnostics13121983.
[27] Balakrishnan C, Ambeth Kumar VD. IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks. Diagnostics (Basel). 2023 Feb 18;13(4):775. doi: 10.3390/diagnostics13040775. PMID: 36832263; PMCID: PMC9955174.
[28] Hemamalini, Selvamani, and Visvam Devadoss Ambeth Kumar. 2022. "Outlier Based Skimpy Regularization Fuzzy Clustering Algorithm for Diabetic Retinopathy Image Segmentation" Symmetry 14, no. 12: 2512. https://doi.org/10.3390/sym14122512.
[29]Kumar, V.D.A., Sharmila, S., Kumar, A. et al. A novel solution for finding postpartum haemorrhage using fuzzy neural techniques. Neural Comput & Applic 35, 23683–23696 (2023). https://doi.org/10.1007/s00521-020-05683-z
[30] V. D. A. Kumar, M. Raghuraman, A. Kumar, M. Rashid, S. Hakak and M. P. K. Reddy, "Green-Tech CAV: Next Generation Computing for Traffic Sign and Obstacle Detection in Connected and Autonomous Vehicles," in IEEE Transactions on Green Communications and Networking, vol. 6, no. 3, pp. 1307-1315, Sept. 2022, doi: 10.1109/TGCN.2022.3162698.
[31] Ahmed Abdelaziz, Alia N. Mahmoud, Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion, Journal of Fusion: Practice and Applications, Vol. 8 , No. 2 , (2022) : 08-15 (Doi : https://doi.org/10.54216/FPA.080201)
[32] Anil Audumbar Pise, Ganesh Shivaji Pise, Saurabh Singh, Hemachandran K., Jude Imuede, Sandip Shinde, An Improved K-Means Clustering Process Solicitation for Mine Blood Donors Information, Journal of Journal of Cybersecurity and Information Management, Vol. 13 , No. 1 , (2024) : 08-16 (Doi : https://doi.org/10.54216/JCIM.130101)
[33] Dai D, Dong C, Xu S, Yan Q, Li Z, Zhang C, Luo N. Ms RED: a novel multi-scale residual encoding and decoding network for skin lesion segmentation. Med Image Anal. 2022;75:102293
[34] Tang P, Yan X, Liang Q, ZhangD. AFLN-DGCL: adaptive feature learning network with difficulty-guided curriculum learning for skin lesion segmentation. Appl Soft Comput. 2021;110:107656.
[35] Mahbod A, Tschandl P, Langs G, Ecker R, Ellinger I. The effects of skin lesion segmentation on the performance of dermatoscopic image classification. Comput Met Progr Biomed. 2020;197:105725
[36] Shetty B, Fernandes R, Rodrigues AP, Chengoden R, Bhattacharya S, Lakshmana K. Skin lesion classification of dermoscopic images using machine learning and convolutional neural network. Sci Rep. 2022;12(1):18134
[37] Ahmed Abdelhafeez, Hoda K. Mohamed, Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 1 , (2023) : 33-42 (Doi : https://doi.org/10.54216/JISIoT.080103)
[38] P. Sherubha, P Amudhavalli, SP Sasirekha, “Clone attack detection using random forest and multi-objective cuckoo search classification”, International Conference on Communication and Signal Processing (ICCSP), pp. 0450-0454, 2019.
[39] S. Dinesh, K. Maheswari, B. Arthi, P. Sherubha, A. Vijay, S. Sridhar, T. Rajendran, and Yosef Asrat Waji, “Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms”, Hindawi Journal of Healthcare Engineering, Volume 2022.
