DVFNet: a deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images
Open accessAbstract
Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. In this study, we presented a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. DVFNet achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the model's accuracy. Healthcare experts utilize the DVFNet model to detect skin cancer at an early clinical stage.
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Naeem and Anees's DVFNet got a score of 98.32% on the ISIC 2019 dataset, with precision 98.23%, recall 98.19%, and F1 score 98.23%, outperforming VGG-16, AlexNet, and ResNet-50.
“Traditional deep learning models like VGG-16, AlexNet, and ResNet-50 were not as accurate as this model, which got a score of 98.32% on the ISIC 2019 dataset. It also had a high precision of 98.23%, recall of 98.19%, and F1 score of 98.23%”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [36]
Table 3 in §4 reports 98.32% accuracy and 98.23% precision for DVFNet with SMOTE TOMEK on the ISIC 2019 dataset, exceeding the accuracies of VGG-16, AlexNet, and ResNet-50. But it reports 98.23% recall and 98.19% F1-score; the claim reverses those two values. Table 4 in §4.4 confirms the same metric assignments. (LLM, conf=0.92)
Experimental — accuracy not yet measuredNaeem and Anees introduced DVFNet, a deep learning-based model for dermoscopy images, achieving AUC 98.32% on the ISIC 2019 dataset, being more precise, recall, and F1 score than VGG-16 and AlexNet, with 98.23, 98.19, and 98.23%, respectively.
“Lastly, Naeem and Anees (36) introduced DVFNet, a deep learning-based model for dermoscopy images, achieving an AUC of 98.32% on the ISIC 2019 dataset, being more precise, recall, and F1 score than VGG-16 and AlexNet, with 98.23, 98.19, and 98.23%, respectively.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [36]
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Cites
Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning
Han, H., Wang, W.-Y., & Mao, B.-H. (2005) · Lecture notes in computer science · doi:10.1007/11538059_91 · Cited by 4,331 · see citing works
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
Codella, N., Rotemberg, V., Tschandl, P., Celebi, M. E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.03368 · Cited by 984 · 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
Goyal, M., Knackstedt, T., Yan, S., & Hassanpour, S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104065 · Cited by 461 · see citing works
Epidemiology of Skin Cancer: Update 2019
Leiter, U., Keim, U., & Garbe, C. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-46227-7_6 · Cited by 459 · see citing works
Ali, M. S., Miah, M. S., Haque, J., Rahman, M. M., & Islam, M. K. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100036 · Cited by 415 · see citing works
Ali, K., Shaikh, Z. A., Khan, A. A., & Laghari, A. A. (2021) · Neuroscience Informatics · doi:10.1016/j.neuri.2021.100034 · Cited by 371 · see citing works
Shetty, B., Fernandes, R., Rodrigues, A. P., Chengoden, R., Bhattacharya, S., & Lakshmanna, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-22644-9 · Cited by 290 · see citing works
BCN20000: Dermoscopic Lesions in the Wild
Combalia, M., Codella, N., Rotemberg, V. M., Helba, B., Vilaplana, V., Reiter, O., Carrera, C., Barreiro, A., Halpern, A. C., Puig, S., et al. (2019) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.48550/arxiv.1908.02288 · Cited by 285 · see citing works
Hybrid convolutional neural networks with SVM classifier for classification of skin cancer
Keerthana, D., Venugopal, V., Nath, M. K., & Mishra, M. (2022) · Biomedical Engineering Advances · doi:10.1016/j.bea.2022.100069 · Cited by 280 · see citing works
Jones, O. T., Matin, R. N., van der Schaar, M., Prathivadi Bhayankaram, K., Ranmuthu, C. K. I., Islam, M. S., Behiyat, D., Boscott, R., Calanzani, N., Emery, J., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00023-1 · Cited by 272 · see citing works
Lee, S. W. (2022) · Life Cycle · doi:10.54724/lc.2022.e1 · Cited by 265 · see citing works
Saba, T., Khan, M. A., Rehman, A. U., & Larabi-Marie-Sainte, S. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1413-3 · Cited by 250 · see citing works
Tahir, M., Naeem, A., Malik, H., Tanveer, J., Naqvi, R. A., & Lee, S.-W. (2023) · Cancers · doi:10.3390/cancers15072179 · Cited by 240 · see citing works
Mridha, K., Uddin, M. M., Shin, J., Khadka, S., & Mridha, M. F. (2023) · IEEE Access · doi:10.1109/access.2023.3269694 · Cited by 237 · see citing works
Deep Learning Solutions for Skin Cancer Detection and Diagnosis
Nahata, H., & SINGH, S. P. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-40850-3_8 · Cited by 222 · see citing works
Shah, A., Shah, M., Pandya, A., Sushra, R., Sushra, R., Mehta, M., Patel, K., & Patel, K. (2023) · Clinical eHealth · doi:10.1016/j.ceh.2023.08.002 · Cited by 196 · see citing works
Naeem, A., Farooq, M. S., Khelifi, A., & Abid, A. (2020) · IEEE Access · doi:10.1109/access.2020.3001507 · Cited by 189 · see citing works
Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images
Kaur, R., GholamHosseini, H., Sinha, R., & Lindén, M. (2022) · Sensors · doi:10.3390/s22031134 · Cited by 186 · see citing works
Skin cancer detection using ensemble of machine learning and deep learning techniques
Tembhurne, J. V., Hebbar, N., Patil, H. Y., & Diwan, T. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14697-3 · Cited by 185 · see citing works
Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks
Bajwa, M. N., Muta, K., Malik, M. I., Siddiqui, S. A., Braun, S. A., Homey, B., Dengel, A. R., & Ahmed, S. (2020) · Applied Sciences · doi:10.3390/app10072488 · Cited by 183 · see citing works
Skin disease detection using deep learning
Inthiyaz, S., Altahan, B. R., Ahammad, S. H., et al. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103361 · Cited by 173 · see citing works
Skin cancer disease images classification using deep learning solutions
Mijwil, M. M. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10952-7 · Cited by 154 · see citing works
Melanoma detection using deep learning-based classifications
Alwakid, G., Gouda, W., Humayun, M., & Sama, N. U. (2022) · Healthcare · doi:10.3390/healthcare10122481 · Cited by 153 · see citing works
Thanh, D. N. H., Prasath, V. B. S., Hiếu, L. M., & Hiền, N. N. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00316-x · Cited by 151 · see citing works
Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application
Kousis, I., Perikos, I., Hatzilygeroudis, I., & Virvou, M. K. (2022) · Electronics · doi:10.3390/electronics11091294 · Cited by 148 · see citing works
Malik, H., Anees, T., din, M.-Z., & Naeem, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13843-7 · Cited by 141 · see citing works
Self-citationBhatt, H., Shah, V., Shah, K., Shah, R., & Shah, M. (2022) · Intelligent Medicine · doi:10.1016/j.imed.2022.08.004 · Cited by 138 · see citing works
Carli, P., Quercioli, E., Sestini, S., Stante, M., Ricci, L., Brunasso, A. M. G., & GIORGI, V. D. (2003) · British Journal of Dermatology · doi:10.1046/j.1365-2133.2003.05023.x · Cited by 132 · see citing works
Usefulness of dermoscopy to improve the clinical and histopathologic diagnosis of skin cancers
Yélamos, O., Braun, R. P., Liopyris, K., et al. (2018) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2018.07.072 · Cited by 128 · see citing works
Naeem, A., Anees, T., Fiza, M., Naqvi, R. A., & Lee, S. W. (2022) · Sensors · doi:10.3390/s22155652 · Cited by 128 · see citing works
Self-citationSkin Cancer Classification Using Deep Spiking Neural Network
Gilani, S. Q., Syed, T., Umair, M., & Marques, O. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00776-2 · Cited by 121 · see citing works
Dermoscopy of Melanoma and Non-melanoma Skin Cancers
Kato, J., Horimoto, K., Sato, S., Minowa, T., & Uhara, H. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00180 · Cited by 116 · see citing works
Deep learning-based computer aided diagnosis model for skin cancer detection and classification
Adla, D., Reddy, G. V. R., Nayak, P., & Karuna, G. (2021) · Distributed and Parallel Databases · doi:10.1007/s10619-021-07360-z · Cited by 103 · see citing works
Naeem, A., Anees, T., Naqvi, R. A., & Loh, W.-K. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020275 · Cited by 103 · see citing works
Self-citationAutomatic Classification of Melanoma Skin Cancer with Deep Convolutional Neural Networks
Aljohani, K., & Turki, T. (2022) · AI · doi:10.3390/ai3020029 · Cited by 99 · see citing works
Co-Attention Fusion Network for Multimodal Skin Cancer Diagnosis
He, X., Wang, Y., Zhao, S., & Chen, X. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108990 · Cited by 97 · see citing works
Olayah, F., Senan, E. M., Ahmed, I. A., & Awaji, B. (2023) · Diagnostics · doi:10.3390/diagnostics13071314 · Cited by 97 · see citing works
Gregoor, A. M. S., Sangers, T. E., Bakker, L. J., Hollestein, L. M., Groot, C. A. U., Nijsten, T. E. C., & Wakkee, M. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00831-w · Cited by 92 · see citing works
Early Detection of Skin Cancer Using Melanoma Segmentation technique
Sreelatha, T., Subramanyam, M. V., & Prasad, M. N. G. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1334-1 · Cited by 91 · see citing works
Ömeroğlu, A. N., Mohammed, H. M. A., Oral, E. A., & Aydın, S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105897 · Cited by 85 · see citing works
Concatenated Xception-ResNet50 — A novel hybrid approach for accurate skin cancer prediction
Panthakkan, A., Anzar, S. M., Jamal, S., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106170 · Cited by 78 · see citing works
Iqbal, I., Walayat, K., Kakar, M. U., & Ma, J. (2022) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2022.200149 · Cited by 76 · see citing works
Qian, S., Ren, K., Zhang, W., & Ning, H. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107166 · Cited by 75 · see citing works
Bansal, P., Garg, R., & Soni, P. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2022.108060 · Cited by 70 · see citing works
Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique
Viknesh, C. K., Kumar, P. N., Seetharaman, R., & Anitha, D. (2023) · Diagnostics · doi:10.3390/diagnostics13213313 · Cited by 62 · see citing works
Enhancement in Skin Cancer Detection using Image Super Resolution and Convolutional Neural Network
Lembhe, A., Motarwar, P., Patil, R., et al. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.412 · Cited by 56 · see citing works
Dong, C., Dai, D., Zhang, Y., Zhang, C., Li, Z., & Xu, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106321 · Cited by 54 · see citing works
Le, D. N. T., Le, H. X., Ngo, L. T., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.05977 · Cited by 52 · see citing works
Rahman, M. M., Nasir, M. K., Nur-A-Alam, M., & Khan, M. S. I. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100341 · Cited by 49 · see citing works
On the Overlap Between Grad-CAM Saliency Maps and Explainable Visual Features in Skin Cancer Images
Nunnari, F., Kadir, M. A., & Sonntag, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84060-0_16 · Cited by 48 · see citing works
Optimal Skin Cancer Detection Model Using Transfer Learning and Dynamic-Opposite Hunger Games Search
Dahou, A., Aseeri, A. O., Mabrouk, A., Ibrahim, R. A., Al‐Betar, M. A., & Elaziz, M. A. (2023) · Diagnostics · doi:10.3390/diagnostics13091579 · Cited by 47 · see citing works
Riaz, S., Naeem, A., Malik, H., Naqvi, R. A., & Loh, W.-K. (2023) · Sensors · doi:10.3390/s23208457 · Cited by 45 · see citing works
Self-citationTang, P., Yan, X., Liang, Q., & Zhang, D. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107656 · Cited by 40 · see citing works
A Technical Review Report on Deep Learning Approach for Skin Cancer Detection and Segmentation
Keerthana, D., & Nath, M. K. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-8335-3_9 · Cited by 37 · see citing works
Intelligent Transportation Systems in Smart City: A Systematic Survey
Hassan, M. A., Javed, R., Farhatullah, Granelli, F., Gen, X., Rizwan, M., Ali, S. H., Junaid, H., & Ullah, S. (2023) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai57502.2023.10089543 · Cited by 35 · see citing works
An efficient technique for skin cancer classification using deep learning
Ashraf, R., Kiran, I., Mahmood, T., Butt, A. U. R., Razzaq, N., & Farooq, Z. (2020) · IEEE International Multitopic Conference (INMIC) · doi:10.1109/inmic50486.2020.9318164 · Cited by 34 · see citing works
Maron, R. C., Hekler, A., Krieghoff‐Henning, E., Schmitt, M., Schlager, J. G., Utikal, J. S., & Brinker, T. J. (2021) · Journal of Medical Internet Research · doi:10.2196/21695 · Cited by 32 · see citing works
Oxidative-Stress-Sensitive microRNAs in UV-Promoted Development of Melanoma
Pecorelli, A., & Valacchi, G. (2022) · Cancers · doi:10.3390/cancers14133224 · Cited by 26 · see citing works
Razmjooy, N., & Arshaghi, A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104984 · Cited by 26 · see citing works
Ge, B., Yang, H., Ma, P., et al. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104316 · Cited by 25 · see citing works
Real-time SIFT-based object recognition system
Wang, Z., Han, X., He, W., Wen, F., & Yuan, K. (2013) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma.2013.6618111 · Cited by 23 · see citing works
A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics
Bakheet, S., Alsubai, S., Elnagar, A. M., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13081474 · Cited by 22 · see citing works
Ding, H. W., Huang, Q., & Alkhayyat, A. H. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105870 · Cited by 21 · see citing works
Biopsy guided by dermoscopy in cutaneous pigmented lesion - Case report
Bomm, L., Benez, M., Pineiro‐Maceira, J. M., Succi, I. C. B., & Alves, M. D. F. G. S. (2013) · Anais Brasileiros de Dermatologia · doi:10.1590/s0365-05962013000100020 · Cited by 20 · see citing works
3D-HOG Features –Based Classification using MRI Images to Early Diagnosis of Alzheimer’s Disease
Sarwinda, D., & Bustamam, A. (2018) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2018.8466524 · Cited by 17 · see citing works
Detection of Melanoma from Skin Lesion Images using Deep Learning Techniques
Shah, V., Autee, P., & Sonawane, P. (2020) · doi:10.1109/icdse50459.2020.9310131 · Cited by 17 · see citing works
Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques
Shehzadi, S., Hassan, M. A., Rizwan, M., Kryvinska, N., & Karovič, V. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3823350 · Cited by 16 · see citing works
Mayrose, H., Sampathila, N., Bairy, G. M., Belurkar, S., Saravu, K., Basu, A., & Khan, S. (2021) · Expert Systems · doi:10.1111/exsy.12904 · Cited by 15 · see citing works
Modified anisotropic diffusion and level-set segmentation for breast cancer
Olota, M., Alsadoon, A., Alsadoon, O. H., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16021-5 · Cited by 15 · see citing works
Machine Learning Models for the Classification of Skin Cancer
Arooj, S., Khan, M. F., Khan, M. A., Khan, M. S., & Taleb, N. (2022) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats54253.2022.9759054 · Cited by 5 · see citing works
Desale, R. P., & Patil, P. S. (2023) · The Imaging Science Journal · doi:10.1080/13682199.2023.2229018 · Cited by 4 · see citing works
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Chakraborty, C., Achar, U., Nayek, S., Achar, A., & Mukherjee, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-76852-6 · Cited by 12 · see citing works
Revolutionizing dermatopathology using AI in skin diagnostics: scoping review
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- Authors
- Naeem, A., & Anees, T.
- Affiliations
- University of Management and Technology (PK)
- Corresponding author
- Naeem, Ahmad
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2024-03-20
- Volume
- 19
- Issue
- 3
- Pages
- e0297667
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC10954125
- OpenAlex
- W4393026478
- Cited by
- 48
- References
- 74
- Citations by year
- 2026: 11 · 2025: 25 · 2024: 12
- FWCI
- 8.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- skin cancer classification, dermoscopic images, deep feature fusion, VGG19, histogram of oriented gradients, anisotropic diffusion, image denoising, SMOTE-Tomek, class imbalance, ISIC 2019, early detection
- MeSH terms
- Algorithms, Humans, Image Processing, Computer-Assisted, Melanoma, Skin Neoplasms, Dermoscopy
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