Artificial intelligence-driven enhanced skin cancer diagnosis: leveraging convolutional neural networks with discrete wavelet transformation
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Abstract Background Artificial intelligence (AI) has shown great promise in the field of healthcare as a means of improving the diagnosis of skin cancer. The objective of this research is to enhance the precision and effectiveness of skin cancer identification by the incorporation of convolutional neural networks (CNNs) and discrete wavelet transformation (DWT). Making use of AI-driven techniques has the potential to completely transform the diagnosis process by providing quicker and more accurate evaluations of skin lesions. In an effort to improve dermatology and give physicians reliable resources for early and precise skin cancer diagnosis, this work explores the combination of CNNs with DWT. Methods The accurate and timely classification of skin cancer lesions plays a crucial role in early diagnosis and effective treatment. In this, we propose a novel approach for skin cancer classification using discrete wavelet transformation (DWT). The DWT is employed to extract relevant features from skin lesion images, which are then used to train a classification model. The effectiveness of the suggested approach is assessed through the examination of a dataset of skin lesion images with known classes (malignant or benign). Results The outcomes of the experiment demonstrate that the suggested model successfully attained a classification result of sensitivity as 94% and specificity as 91% when compared with artificial neural network (ANN) and multilayer perceptron methods. Conclusions The HAM 10000 dataset is employed to explore and evaluate the effectiveness of the proposed model, leading to improved accuracy compared to the existing machine learning algorithms in utilization. The results demonstrate the effectiveness of the DWT-based approach in accurately classifying skin cancer lesions, thus aiding in early detection and diagnosis.
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The rising incidence of skin diseases worldwide drives a pressing demand for practical, accurate, and timely diagnostic approaches.
“The reasons for investigating the application of AI models in dermatopathology are driven by the rising incidence of skin diseases worldwide, and therefore, the pressing demand for practical, accurate, and timely diagnostic approaches (14).”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [14]
The Introduction (page unavailable) says skin cancer deaths have increased and that early classification is essential; it also describes accurate, timely diagnosis using AI-assisted methods. It does not establish a rising worldwide incidence of skin diseases generally. Increased deaths from skin cancer are not the same as increased incidence of all skin diseases. (LLM, conf=0.86)
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Cites
Tschandl, P., Rosendahl, C., & Kittler, H. (2018) · Scientific Data · doi:10.1038/sdata.2018.161 · Cited by 3,628 · see citing works
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620 · see citing works
Skin cancer detection: A review using deep learning techniques
Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A. H. M., Alraddadi, M. O., & Mahnashi, M. H. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18105479 · Cited by 597 · see citing works
Skin lesion classification with ensembles of deep convolutional neural networks
Harangi, B. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.08.006 · Cited by 451 · see citing works
Cancer Diagnosis Using Deep Learning: A Bibliographic Review
Munir, K., Elahi, H., Ayub, A., Frezza, F., & Rizzi, A. (2019) · Cancers · doi:10.3390/cancers11091235 · Cited by 430 · see citing works
The skin cancer classification using deep convolutional neural network
Dorj, U.-O., Lee, K.-K., Choi, J., & Lee, M. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5714-1 · Cited by 406 · see citing works
Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning
Gouda, W., Sama, N. U., Al-Waakid, G., Humayun, M., & Jhanjhi, N. Z. (2022) · Healthcare · doi:10.3390/healthcare10071183 · Cited by 402 · see citing works
Classification of skin lesions using transfer learning and augmentation with Alex-net
Hosny, K. M., Kassem, M. A. A., & Foaud, M. M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217293 · Cited by 375 · see citing works
Superior skin cancer classification by the combination of human and artificial intelligence
Hekler, A., Utikal, J. S., Enk, A. H., Hauschild, A., Weichenthal, M., Maron, R. C., Berking, C., Haferkamp, S., Klode, J., Schadendorf, D., et al. (2019) · European Journal of Cancer · doi:10.1016/j.ejca.2019.07.019 · Cited by 357 · see citing works
Adegun, A. A., & Viriri, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09865-y · Cited by 331 · see citing works
Skin Lesion Classification Using Convolutional Neural Network With Novel Regularizer
Albahar, M. A. (2019) · IEEE Access · doi:10.1109/access.2019.2906241 · Cited by 298 · see citing works
Guan, Q., Wang, Y., Ping, B., Li, D., Du, J., Yu, Q., Lu, H., Wan, X., & Xiang, J. (2019) · Journal of Cancer · doi:10.7150/jca.28769 · Cited by 290 · see citing works
Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers
Murugan, A., Nair, S., & Kumar, K. P. S. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1400-8 · Cited by 265 · see citing works
Skin cancer detection from dermoscopic images using deep learning and fuzzy k ‐means clustering
Nawaz, M., Mehmood, Z., Nazir, T., Naqvi, R. A., Rehman, A. U., Iqbal, M., & Saba, T. (2021) · Microscopy Research and Technique · doi:10.1002/jemt.23908 · Cited by 263 · see citing works
Convolutional Neural Networks Using MobileNet for Skin Lesion Classification
Sae-Lim, W., Wettayaprasit, W., & Aiyarak, P. (2019) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse.2019.8864155 · Cited by 221 · see citing works
Diagnosis of skin cancer using machine learning techniques
Murugan, A., Nair, S., Preethi, A. A. P., & Kumar, K. P. S. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.103727 · Cited by 174 · see citing works
Deep Learning-Based Transfer Learning for Classification of Skin Cancer
Jain, S., Singhania, U., Tripathy, B., Nasr, E. A., Aboudaif, M. K., & Kamrani, A. K. (2021) · Sensors · doi:10.3390/s21238142 · Cited by 169 · see citing works
Deep Learning–Based Methods for Automatic Diagnosis of Skin Lesions
El-Khatib, H., Popescu, D., & Ichim, L. (2020) · Sensors · doi:10.3390/s20061753 · Cited by 159 · see citing works
Skin Cancer Detection using Machine Learning Techniques
Vidya, M., & Karki, M. V. (2020) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct50063.2020.9198489 · Cited by 155 · see citing works
Prostate Cancer Detection Using Deep Learning and Traditional Techniques
Iqbal, S., Siddiqui, G. F., Rehman, A. U., Hussain, L., Saba, T., Tariq, U., & Abbasi, A. A. (2021) · IEEE Access · doi:10.1109/access.2021.3057654 · Cited by 131 · see citing works
A Deep Learning Approach to Skin Cancer Detection in Dermoscopy Images
Ameri, A. (2020) · Journal of Biomedical Physics and Engineering · doi:10.31661/jbpe.v0i0.2004-1107 · Cited by 112 · see citing works
A survey on automated melanoma detection
Okur, E., & Türkan, M. (2018) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2018.04.028 · Cited by 101 · see citing works
The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers
Fujisawa, Y., Inoue, S., & Nakamura, Y. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00191 · Cited by 84 · see citing works
Li, Y., Li, C., Li, X., Wang, K., Rahaman, M. M., Sun, C., Chen, H., Wu, X., Zhang, H., & Wang, Q. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09591-w · Cited by 76 · see citing works
Impact of UV Radiation on Genome Stability and Human Health
Roy, S. (2017) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-56017-5_17 · Cited by 75 · see citing works
Deep Learning to Detect Skin Cancer using Google Colab
Kanani, P., & Padole, M. (2019) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.f8587.088619 · Cited by 66 · see citing works
Deep Neural Network Based Mobile Dermoscopy Application for Triaging Skin Cancer Detection
Ech-Cherif, A., Misbhauddin, M., & Ech-Cherif, M. E.-A. (2019) · International Conference on Computer Applications & Information Security (ICCAIS) · doi:10.1109/cais.2019.8769517 · Cited by 64 · see citing works
Alom, M. Z., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon.2018.8556737 · Cited by 53 · see citing works
Detecting biomarkers from microarray data using distributed correlation based gene selection
Shukla, A. K., & Tripathi, D. (2020) · Genes & Genomics · doi:10.1007/s13258-020-00916-w · Cited by 38 · see citing works
Gouabou, A. C. F., Damoiseaux, J.-L., Monnier, J., et al. (2021) · Sensors · doi:10.3390/s21123999 · Cited by 37 · see citing works
An accurate and noninvasive skin cancer screening based on imaging technique
Rajput, G., Agrawal, S., Raut, G., & Vishvakarma, S. K. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22616 · Cited by 36 · see citing works
Kim, C.-I., Hwang, S.-M., Park, E.-B., Won, C., & Lee, J.-H. (2021) · Sensors · doi:10.3390/s21165551 · Cited by 33 · see citing works
Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer DetectionPreprint
Rahman, M. M., Nasir, M. K., A-Alam, N., Khan, S. I., Band, S. S., Dehzangi, A., Beheshti, A., & Alinejad‐Rokny, H. (2022) · Preprints.org · doi:10.20944/preprints202201.0258.v1 · Cited by 27 · see citing works
Haar‐Wavelet‐Based Just Noticeable Distortion Model for Transparent Watermark
Ko, L.-T., Chen, J.-E., Hsin, H., et al. (2011) · Mathematical Problems in Engineering · doi:10.1155/2012/635738 · Cited by 17 · see citing works
A Skin Cancer Classification Method Based on Discrete Wavelet Down-Sampling Feature Reconstruction
Wu, Q., Yao, Y., & Zhang, X. (2023) · Electronics · doi:10.3390/electronics12092103 · Cited by 15 · see citing works
Deteksi Dini Kanker Kulit menggunakan K-NN dan Convolutional Neural Network
Savera, T. R., Suryawan, W. H., & Setiawan, A. W. (2020) · Jurnal Teknologi Informasi dan Ilmu Komputer · doi:10.25126/jtiik.2020702602 · Cited by 9 · see citing works
Early Detection of Melanoma Skin Cancer Using Image Processing and Deep Learning
Shah, S. A. R., Ahmed, I., Mujtaba, G., et al. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-1053-1_25 · Cited by 5 · see citing works
Segmentation Preprocessing and Deep Learning Based Classification of Skin Lesions
Zhang, X., & Zhao, S. (2018) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2018.2448 · Cited by 3 · see citing works
MDDC: melanoma detection using discrete wavelet transform and convolutional neural network
Asadi, O., Yekkalam, A., & Manthouri, M. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-04381-z · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 17
- Ansari UB, Sarode T (2017) Skin cancer detection using image processing. Int Res J Eng Technol 4(4):2875-2881
- Muthukumar K, Gowthaman P, Venkatachalam M, Saroja M, Pradheep N (2019) GTCM based skin lesion melanoma disease detection approach for optimal classification of medical images. Int J Recent Technol Eng 7(3):1-2
- Milton MA (2019) Automated skin lesion classification using ensemble of deep neural networks in isic 2018: skin lesion analysis towards melanoma detection challenge. arXiv preprint arXiv: 1901. 10802
- Vijayalakshmi MM (2019) Melanoma skin cancer detection using image processing and machine learning. Int J Trend Sci Res Dev 3(4):780-784
- Ravichandran D, Nimmatoori R, Ahamad MG (2016) Mathematical rep- resentations of 1D, 2D and 3D wavelet transform for image coding. Int J Adv Comput Theory Eng 5(3):20-27
- Masood A, Ali A-J (2013) Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int J Biomed Imaging 30:2013
- Kaur R, Kumar P, Babbar G (2019) An enhanced and automatic skin cancer detection using K-mean and PSO technique. Int J Innov Technol Explor Eng 8(9):634-639
- Mane SS, Shinde SV (2017) Different techniques for skin cancer detection using dermoscopy images. Int J Comput Sci Eng 5(12):165-170
- Patel I, Patel S, Patel A (2019) Dermoscopic image classification using image processing technique for melanoma detection. Int J Res Advent Technol 23(2):97-103
- Zhang X, Wang S, Liu J, Tao C (2018) Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge. BMC Med Inform Decis Mak 18(2):69-76
- Le DN, Le HX, Ngo LT, Ngo HT (2020) Transfer learning with class- weighted and focal loss function for automatic skin cancer classification. arXiv preprint arXiv: 2009. 05977
- Ali MS, Miah MS, Haque J, Rahman MM, Islam MK (2021) An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Mach Learn Appl 15(5):100036
- Barnouti NH, Sabri ZS, Hameed KL (2018) Digital watermarking based on DWT (discrete wavelet transform) and DCT (discrete cosine transform). Int J Eng Technol 7(4):4825-4829
- Lopez AR, Giro-i-Nieto X, Burdick J, Marques O (2017) Skin lesion clas- sification from dermoscopic images using deep learning techniques. In: 2017 13th IASTED international conference on biomedical engineering (BioMed). IEEE, pp 49-54
- Jaiswar S, Kadri M, Gatty V (2015) Skin cancer detection using digital image processing. Int J Sci Eng Res 3(6):138-140
- Alom MZ, Aspiras T, Taha TM, Asari VK (2019) Skin cancer segmentation and classification with NABLA-N and inception recurrent residual convo- lutional networks. arXiv preprint arXiv: 1904. 11126
- Thsper P, Singh A (2019) A survey of lesion detection using dermoscopy image analysis. J Gujarat Res Soc 21(6):129-143
Cited by
Sorted by citations
Revolutionizing dermatopathology using AI in skin diagnostics: scoping review
Rammal, R., Mohy U Din, A., & Alam, T. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1614681 · Cited by 1 · see citing works
- Authors
- Claret, S. P. A., Dharmian, J. P., & Manokar, A. M.
- Affiliations
- SRM Institute of Science and Technology (IN)
- B.S. Abdur Rahman Crescent Institute of Science & Technology (IN)
- Corresponding author
- Manokar, A. Muthu
- ORCID
- Manokar, A. Muthu
- Venue
- Egyptian Journal of Medical Human Genetics
- Published
- 2024-04-09
- Volume
- 25
- Issue
- 1
- ISSN
- 1110-8630 (ISSN-L)
- 2090-2441
- OpenAlex
- W4394613312
- Cited by
- 40
- References
- 40
- Citations by year
- 2026: 14 · 2025: 20 · 2024: 6
- FWCI
- 6.87
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Egyptian Journal of Medical Human Genetics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- skin cancer diagnosis, skin lesion classification, convolutional neural networks, HAM10000 dataset, feature extraction, early cancer detection
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