research-article

Skin lesion classification using modified deep and multi-directional invariant handcrafted features

Authors:

Jitesh Pradhan,

Muhammad Khurram KhanAuthors Info & Claims

Volume 231, Issue C

https://doi.org/10.1016/j.jnca.2024.103949

Published: 18 November 2024 Publication History

Abstract

Skin lesions encompass various skin conditions, including cancerous growths resulting from uncontrolled proliferation of skin cells. Globally, this disease affects a significant portion of the population, with millions of fatalities recorded. Over the past three decades, there has been a concerning escalation in diagnosed cases of skin cancer. Early detection is crucial for effective treatment, as late diagnosis significantly heightens mortality risk. Existing research often focuses on either handcrafted or deep features, neglecting the diverse textural and structural properties inherent in skin lesion images. Additionally, reliance on a single optimizer in CNN-based schemes poses efficiency challenges. To tackle these issues, this paper presents two novel approaches for classifying skin lesions in dermoscopic images to assess cancer severity. The first approach enhances classification accuracy by leveraging a modified VGG-16 network and employing both RMSProp and Adam optimizers. The second approach introduces a Hybrid CNN Model, integrating deep features from the modified VGG-16 network with handcrafted color and multi-directional texture features. Color features are extracted using a non-uniform cumulative probability-based histogram method, while texture features are derived from a 45∘ rotated complex wavelet filter-based dual-tree complex wavelet transform. The amalgamated features facilitate accurate prediction of skin lesion classes. Evaluation on ISIC 2017 skin cancer classification challenge images demonstrates significant performance enhancements over existing techniques.

References

[1]

Abdul-Aziz G.A., Aly A.S., Trialing a smart face-recognition computer system to recognize lost people visiting the two holy mosques, Arab J. Forensic Sci. Forensic Med. 1 (8) (2018) 1120–1132.

[2]

Albahri A.S., Alwan J.K., Taha Z.K., Ismail S.F., Hamid R.A., Zaidan A., Albahri O.S., Zaidan B., Alamoodi A.H., Alsalem M., IoT-based telemedicine for disease prevention and health promotion: State-of-the-art, J. Netw. Comput. Appl. 173 (2021).

Digital Library

[3]

Alhudhaif A., Almaslukh B., Aseeri A.O., Guler O., Polat K., A novel nonlinear automated multi-class skin lesion detection system using soft-attention based convolutional neural networks, Chaos Solitons Fractals 170 (2023).

[4]

Ali S.N., Ahmed M.T., Paul J., Jahan T., Sani S., Noor N., Hasan T., Monkeypox skin lesion detection using deep learning models: A feasibility study, 2022, arXiv preprint arXiv:2207.03342.

[5]

Altamimi A., Alrowais F., Karamti H., Umer M., Cascone L., Ashraf I., An improved skin lesion detection solution using multi-step preprocessing features and NASNet transfer learning model, Image Vis. Comput. 144 (2024).

[6]

Barata C., Celebi M.E., Marques J.S., Improving dermoscopy image classification using color constancy, IEEE J. Biomed. Health Inform. 19 (3) (2014) 1146–1152.

[7]

Barata C., Ruela M., Francisco M., Mendonça T., Marques J.S., Two systems for the detection of melanomas in dermoscopy images using texture and color features, IEEE Syst. J. 8 (3) (2013) 965–979.

[8]

Bochie K., Gilbert M.S., Gantert L., Barbosa M.S., Medeiros D.S., Campista M.E.M., A survey on deep learning for challenged networks: Applications and trends, J. Netw. Comput. Appl. 194 (2021).

[9]

Brinker T.J., Hekler A., Utikal J.S., Grabe N., Schadendorf D., Klode J., Berking C., Steeb T., Enk A.H., Von Kalle C., Skin cancer classification using convolutional neural networks: Systematic review, J. Med. Internet Res. 20 (10) (2018).

[10]

Celebi M.E., Kingravi H.A., Uddin B., Iyatomi H., Aslandogan Y.A., Stoecker W.V., Moss R.H., A methodological approach to the classification of dermoscopy images, Comput. Med. imaging Graph. 31 (6) (2007) 362–373.

[11]

Codella N.C., Gutman D., Celebi M.E., Helba B., Marchetti M.A., Dusza S.W., Kalloo A., Liopyris K., Mishra N., Kittler H., et al., Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC), in: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, 2017, pp. 168–172.

[12]

Esteva A., Kuprel B., Novoa R.A., Ko J., Swetter S.M., Blau H.M., Thrun S., Dermatologist-level classification of skin cancer with deep neural networks, Nature 542 (7639) (2017) 115–118.

[13]

Faizi M.I., Adnan S.M., Improved segmentation model for melanoma lesion detection using normalized cross-correlation-based k-means clustering, IEEE Access (2024).

[14]

Gessert N., Sentker T., Madesta F., Schmitz R., Kniep H., Baltruschat I., Werner R., Schlaefer A., Skin lesion diagnosis using ensembles, unscaled multi-crop evaluation and loss weighting, 2018, arXiv preprint arXiv:1808.01694.

[15]

Ghafoorian M., Mehrtash A., Kapur T., Karssemeijer N., Marchiori E., Pesteie M., Guttmann C.R., de Leeuw F.-E., Tempany C.M., Van Ginneken B., et al., Transfer learning for domain adaptation in MRI: Application in brain lesion segmentation, in: Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20, Springer, 2017, pp. 516–524.

[16]

Glorot X., Bengio Y., Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, 2010, pp. 249–256.

[17]

Gupta O., Raskar R., Distributed learning of deep neural network over multiple agents, J. Netw. Comput. Appl. 116 (2018) 1–8.

[18]

Gutman D., Codella N.C., Celebi E., Helba B., Marchetti M., Mishra N., Halpern A., Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC), 2016, arXiv preprint arXiv:1605.01397.

[19]

Gutub A., Shambour M.K., Abu-Hashem M.A., Coronavirus impact on human feelings during 2021 Hajj season via deep learning critical Twitter analysis, J. Eng. Res. 11 (1) (2023).

[20]

Hamd M.H., Essa K.A., Mustansirya A., Skin cancer prognosis based pigment processing, Int. J. Image Process. 7 (3) (2013) 227.

[21]

He, K., Zhang, X., Ren, S., Sun, J., 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1026–1034.

[22]

He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778.

[23]

Hu, J., Shen, L., Sun, G., 2018. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7132–7141.

[24]

Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., 2017. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4700–4708.

[25]

Kashani M.H., Madanipour M., Nikravan M., Asghari P., Mahdipour E., A systematic review of IoT in healthcare: Applications, techniques, and trends, J. Netw. Comput. Appl. 192 (2021).

[26]

Kawahara J., BenTaieb A., Hamarneh G., Deep features to classify skin lesions, in: 2016 IEEE 13th International Symposium on Biomedical Imaging, ISBI, IEEE, 2016, pp. 1397–1400.

[27]

Krizhevsky A., Sutskever I., Hinton G.E., Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst. 25 (2012).

Digital Library

[28]

Lewis K.G., Weinstock M.A., Trends in nonmelanoma skin cancer mortality rates in the United States, 1969 through 2000, J. Invest. Dermatol. 127 (10) (2007) 2323–2327.

[29]

Li Y., Shen L., Skin lesion analysis towards melanoma detection using deep learning network, Sensors 18 (2) (2018) 556.

[30]

Litjens G., Kooi T., Bejnordi B.E., Setio A.A.A., Ciompi F., Ghafoorian M., Van Der Laak J.A., Van Ginneken B., Sánchez C.I., A survey on deep learning in medical image analysis, Med. Image Anal. 42 (2017) 60–88.

[31]

Lopez A.R., Giro-i Nieto X., Burdick J., Marques O., Skin lesion classification from dermoscopic images using deep learning techniques, in: 2017 13th IASTED International Conference on Biomedical Engineering, BioMed, IEEE, 2017, pp. 49–54.

[32]

Mahbod A., Ellinger I., Ecker R., Smedby Ö., Wang C., Breast cancer histological image classification using fine-tuned deep network fusion, in: Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018, Proceedings 15, Springer, 2018, pp. 754–762.

[33]

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. 71 (2019) 19–29.

[34]

Mahbod A., Schaefer G., Wang C., Ecker R., Ellinge I., Skin lesion classification using hybrid deep neural networks, in: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, IEEE, 2019, pp. 1229–1233.

[35]

McGhin T., Choo K.-K.R., Liu C.Z., He D., Blockchain in healthcare applications: Research challenges and opportunities, J. Netw. Comput. Appl. 135 (2019) 62–75.

Digital Library

[36]

Nachbar F., Stolz W., Merkle T., Cognetta A.B., Vogt T., Landthaler M., Bilek P., Braun-Falco O., Plewig G., The ABCD rule of dermatoscopy: High prospective value in the diagnosis of doubtful melanocytic skin lesions, J. Am. Acad. Dermatol. 30 (4) (1994) 551–559.

[37]

Ogudo K.A., Surendran R., Khalaf O.I., Optimal artificial intelligence based automated skin lesion detection and classification model, Comput. Syst. Sci. Eng. 44 (1) (2023).

[38]

Okuboyejo D.A., Olugbara O.O., A review of prevalent methods for automatic skin lesion diagnosis, Open Dermatol. J. 12 (1) (2018).

[39]

Oliveira R.B., Papa J.P., Pereira A.S., Tavares J.M.R., Computational methods for pigmented skin lesion classification in images: Review and future trends, Neural Comput. Appl. 29 (2018) 613–636.

[40]

Rogers H.W., Weinstock M.A., Feldman S.R., Coldiron B.M., Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the US population, 2012, JAMA Dermatol. 151 (10) (2015) 1081–1086.

[41]

Roy P.K., Saumya S., Singh J.P., Banerjee S., Gutub A., Analysis of community question-answering issues via machine learning and deep learning: State-of-the-art review, CAAI Trans. Intell. Technol. 8 (1) (2023) 95–117.

[42]

Serte S., Demirel H., Gabor wavelet-based deep learning for skin lesion classification, Comput. Biol. Med. 113 (2019).

[43]

Shahsavari A., Khatibi T., Ranjbari S., Skin lesion detection using an ensemble of deep models: SLDED, Multimedia Tools Appl. 82 (7) (2023) 10575–10594.

[44]

Simonyan K., Zisserman A., Very deep convolutional networks for large-scale image recognition, 2014, arXiv preprint arXiv:1409.1556.

[45]

Singh A., Satapathy S.C., Roy A., Gutub A., Ai-based mobile edge computing for iot: Applications, challenges, and future scope, Arab. J. Sci. Eng. 47 (8) (2022) 9801–9831.

[46]

Sufi F.K., Alsulami M., Gutub A., Automating global threat-maps generation via advancements of news sensors and AI, Arab. J. Sci. Eng. 48 (2) (2023) 2455–2472.

[47]

Szegedy C., Ioffe S., Vanhoucke V., Alemi A., Inception-v4, inception-resnet and the impact of residual connections on learning, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, 2017.

[48]

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–9.

[49]

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., 2016. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2818–2826.

[50]

Tan M., Le Q., Efficientnet: Rethinking model scaling for convolutional neural networks, in: International Conference on Machine Learning, PMLR, 2019, pp. 6105–6114.

[51]

Varma P.B.S., Paturu S., Mishra S., Rao B.S., Kumar P.M., Krishna N.V., SLDCNet: Skin lesion detection and classification using full resolution convolutional network-based deep learning CNN with transfer learning, Expert Syst. 39 (9) (2022).

[52]

White R., Rigel D.S., Friedman R.J., Computer applications in the diagnosis and prognosis of malignant melanoma, Dermatol. Clin. 9 (4) (1991) 695–702.

[53]

Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K., 2017. Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1492–1500.

[54]

Yan Y., Kawahara J., Hamarneh G., Melanoma recognition via visual attention, in: Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, June 2–7, 2019, Proceedings 26, Springer, 2019, pp. 793–804.

[55]

Zhang X., Wang S., Liu J., Tao C., Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge, BMC Med. Inform. Decis. Mak. 18 (2) (2018) 69–76.

[56]

Zhang J., Xie Y., Xia Y., Shen C., Attention residual learning for skin lesion classification, IEEE Trans. Med. Imaging 38 (9) (2019) 2092–2103.

[57]

Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V., 2018. Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8697–8710.

Index Terms

Skin lesion classification using modified deep and multi-directional invariant handcrafted features
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
    2. Computational biology
      1. Imaging
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection
      2. Computer vision representations
        Appearance and texture representations
  2. Computer graphics

Index terms have been assigned to the content through auto-classification.

Recommendations

Lung Nodule Classification Using Deep Features in CT Images
CRV '15: Proceedings of the 2015 12th Conference on Computer and Robot Vision

Early detection of lung cancer can help in a sharp decrease in the lung cancer mortality rate, which accounts for more than 17% percent of the total cancer related deaths. A large number of cases are encountered by radiologists on a daily basis for ...
Skin Lesion Classification Using Convolutional Neural Networks Based on Multi-Features Extraction
Computer Analysis of Images and Patterns
Abstract
In the recent era, deep learning has become a crucial technique for the detection of various forms of skin lesions. Indeed, Convolutional neural networks (CNN) have became the state-of-the-art choice for feature extraction. In this paper, we ...
Image Classification of Skin Cancer: Using Deep Learning as a Tool for Skin Self-examinations
Mathematical and Computational Oncology
Abstract
Skin cancer is the most common cancer in the United States, and studies indicate that its incidence is rapidly increasing. Regular skin self-examinations enable early cancer detection and intervention and are recommended in addition to clinician-...

Comments

Information & Contributors

Information

Published In

cover image Journal of Network and Computer Applications

Journal of Network and Computer Applications Volume 231, Issue C

Nov 2024

309 pages

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Academic Press Ltd.

United Kingdom

Publication History

Published: 18 November 2024

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 04 Jan 2025

Other Metrics

View Author Metrics

Citations

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents