Abstract
Melanoma skin cancers are most threatening disease. Manual detection of melanomas using dermoscopic images is very time-consuming method which also demands a high level of competence. An accurate and prompt diagnosis needs the development of an intelligent classification system for the detection of skin cancer. This paper implements deep learning models for skin cancer classification and integrates features obtained from several feature extraction methods. Pre-processing, feature extraction, classification, and performance evaluation are phases of proposed approach. Any superfluous noise in the edges is removed during the pre-processing stage. The Gaussian filter method is used to improve image clarity and remove unwanted pixels. The detection of melanoma cells is based on features such as lesion segmentation and colour of images. The contour approach, contrast, Grey scale approaches, lesion segmentation using U-NET are employed for feature extraction. Deep learning-based classifiers such as ResNet50 and CNN architecture are used to classify based on extracted features. Classification techniques use these qualities to identify malignant and affected skin areas. Sensitivity, specificity, accuracy, and F-score are some of the performance measurement criteria used to evaluate the suggested approach. The classifiers are used on the HAM10000 dataset. On the HAM10000 dataset, the suggested framework outperformed existing melanoma detection systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Demir A, Yilmaz F, Kose O (2019) Early detection of skin cancer using deep learning architectures: resnet-101 and inception-v3. In: 2019 medical technologies congress (TIPTEKNO). IEEE, pp 1–4
Schadendorf D, van Akkooi ACJ, Berking C, Griewank KG, Gutzmer R, Hauschild A, Stang A, Roesch A, Ugurel S (2018) Melanoma. The Lancet 392:971–984
Gandini S, Sera F, Cattaruzza MS, Pasquini P, Zanetti R, Masini C, Boyle P, Melchi CF (2005) Meta-analysis of risk factors for cutaneous melanoma: III. Family history, actinic damage and phenotypic factors. Eur J Canc 41:2040–2059
Pham TC, Tran GS, Nghiem TP, Doucet A, Luong CM, Hoang V-D (2019) A comparative study for classification of skin cancer. In: 2019 International conference on system science and engineering (ICSSE). IEEE, pp 267–272
Kittler H, Pehamberger H, Wolff K, Binder M (2002) Diagnostic accuracy of dermoscopy. Lancet Oncol 3:159–165
Jinnai S, Yamazaki N, Hirano Y, Sugawara Y, Ohe Y, Hamamoto R (2020) The development of a skin cancer classification system for pigmented skin lesions using deep learning. Biomolecules 10:1123
Celebi ME, Kingravi HA, Uddin B, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH (2007) A methodological approach to the classification of dermoscopy images. Comput Med Imaging Graph 31:362–373
Barata C, Ruela M, Francisco M, Mendonça T, Marques JS (2013) Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst J 8:965–979
Soenksen LR, Kassis T, Conover ST, Marti-Fuster B, Birkenfeld JS, Tucker-Schwartz J, Naseem A, Stavert RR, Kim CC, Senna MM, Avilés-Izquierdo J, Collins JJ Barzilay R, Gray ML (202) Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images. Sci Trans Med 13:eabb3652
Kawahara, J., BenTaieb, A., Hamarneh, G.: Deep features to classify skin lesions. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, pp 1397–1400
Emara T, Afify HM, Ismail FH, Hassanien AE ()A modified inception-v4 for imbalanced skin cancer classification dataset. In: 2019 14th International conference on computer engineering and systems (ICCES). IEEE, pp 28–33
Matsunaga K, Hamada A, Minagawa A, Koga H (2017) Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble. arXiv preprint arXiv:1703.03108
Han SS, Kim MS, Lim W, Park GH, Park I, Chang SE (2018) Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J Investig Dermatol 138:1529–1538
Kawahara J, Hamarneh G, Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers. In: International workshop on machine learning in medical imaging. Springer, pp 164–171
Menegola A, Fornaciali M, Pires R, Bittencourt FV, Avila S, Valle E (2017) Knowledge transfer for melanoma screening with deep learning. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017). IEEE, pp 297–300
Hintz-Madsen M, Hansen LK, Larsen J, Drzewiecki KT (2001) A probabilistic neural network framework for the detection of malignant melanoma. In: Artificial neural networks in cancer diagnosis, prognosis, and patient management. CRC Press, pp. 141–184
Piccolo D, Ferrari A, Peris K, Daidone R, Ruggeri B Chimenti S (2002) Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study. Br J Dermatol 147:481–486
RB A, Jaleel JA, Salim S (2013) Implementation of ANN classifier using MATLAB for skin cancer detection. Academic Press
Mariam A, Sheha Cairo University, Mai S, Mabrouk MUST University, Amr S, Cairo University (2012) Automatic detection of melanoma skin cancer using texture analysis. Int J Comput Appl 0975-8887
Gessert N, Nielsen M, Shaikh M, Werner R, Schlaefer A (2020) Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data. MethodsX 7:100864
Chaturvedi SS, Gupta K, Prasad PS, Skin lesion analyser: an efficient seven-way multi-class skin cancer classification using MobileNet. In: International conference on advanced machine learning technologies and applications. Springer, pp 165–176
Yao P, Shen S, Xu M, Liu P, Zhang F, Xing J, Shao P, Kaffenberger B, Xu RX (2021) Single model deep learning on imbalanced small datasets for skin lesion classification. IEEE Trans Med Imaging 41:1242–1254
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Joshi, D. (2023). Implementation of Deep Learning Models for Skin Cancer Classification. In: Sharma, S., Subudhi, B., Sahu, U.K. (eds) Intelligent Control, Robotics, and Industrial Automation. RCAAI 2022. Lecture Notes in Electrical Engineering, vol 1066. Springer, Singapore. https://doi.org/10.1007/978-981-99-4634-1_45
Download citation
DOI: https://doi.org/10.1007/978-981-99-4634-1_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4633-4
Online ISBN: 978-981-99-4634-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)