Poonam Dhiman,Shivani Wadhwa,Aryan Choudhary,Amandeep Kaur,Khushpreet Malra,



skin lesions,squeeze net,classification,feature extraction,deep learning,


Skin malignancies are regarded as the most dangerous disease. Skin cancer has recently received much attention among people worldwide. An earlier diagnosis of skin cancer can lower the mortality rate. Skin cancer can be found and identified via dermoscopy. Automated tools using computer-aided diagnosis models become necessary because visually evaluating dermoscopic images is tedious and time-consuming. The healthcare industry has greatly benefited from recent machine learning advancements like deep learning. Modern technical designs and methodologies make detecting this type of cancer possible; however, automated classification in earlier phases is challenging due to the lack of contrast. As a result, a squeeze net algorithm-based automated computer system is developed for diagnosing skin illnesses. The HAM10000 dataset is gathered for skin lesions. Images of the four skin cancer conditions BCC, DF, MEL, BKL, and NV are included in the dataset. With a 92.25% overall accuracy, 85% precision, 84% recall, and 83% F1 score, the proposed dermonet model did well in classifying skin cancer conditions from the image samples.


I. A. Foahom Gouabou, J. Damoiseaux, J. Monnier, R. Iguernaissi, A. Moudafi, and D. Merad. : ‘Ensemble method of convolutional neural networks with directed acyclic graph using dermoscopic images: Melanoma detection application’. Sensors, Vo. 21(12), 3999, 2021. 10.3390/s21123999
II. A. Qureshi, and T. Roos. : ‘Transfer learning with ensembles of deep neural networks for skin cancer detection in imbalanced data sets’. Neural Processing Letters, Vol. 55(4), pp. 4461-4479, 2023. 10.1007/s11063-022-11049-4
III. B. Switzer, I. Puzanov, J. Skitzki, H. Hamad, and M. Ernstoff. : ‘Managing metastatic melanoma in 2022: a clinical review’. JCO Oncology Practice. Vol. 18(5), pp. 335-351, 2022. 10.1200/OP.21.0068
IV. C. Chang, W. Wang, F. Hsu, R. Chen, H. Chan. : ‘AI HAM 10000 Database to Assist Residents in Learning Differential Diagnosis of Skin Cancer’. IEEE 5th Eurasian Conference on Educational Innovation (ECEI). IEEE. pp. 1-3. 2022, February. 10.1109/ECEI53102.2022.9829465
V. C. Kaushal, S. Bhat, D. Koundal, and A. Singla. : ‘Recent trends in computer assisted diagnosis (CAD) system for breast cancer diagnosis using histopathological images’. Irbm Vol. 40(4), pp. 211-227, 2019. 10.1016/j.irbm.2019.06.001
VI. C. Scard, H. Aubert, M. Wargny, L. Martin, and S. Barbarot. : ‘Risk of melanoma in congenital melanocytic nevi of all sizes: A systematic review’. Journal of the European Academy of Dermatology and Venereology. Vol. 37(1), pp. 32-39, 2023. 10.1111/jdv.18581.
VII. F. Mallat, S.Matar, and B. Soutou. : ‘Umbilical seborrheic keratosis-like lesion developing after diode laser hair removal in an 18-year-old patient’. Journal of Cosmetic and Laser Therapy. Vol.25(1-4), pp. 54-56, 2023. 10.1080/14764172.2023.2241690.
VIII. J. Tembhurne, N. Hebbar, H. Patil, and T. Diwan T., : ‘Skin cancer detection using ensemble of machine learning and deep learning techniques’. Multimedia Tools and Applications. Vol. 82. pp. 27501-27524, 2023. 10.1007/s11042-023-14697-3
IX. L. Steele, X. Tan, L. Olabi, B. Gao, J. Tanaka, and H. Williams. : ‘Determining the clinical applicability of machine learning models through assessment of reporting across skin phototypes and rarer skin cancer types: a systematic review’. Journal of the European Academy of Dermatology and Venereology. Vol. 37(4), pp. 657-665, 2023. 10.1111/jdv.18814
X. M. Alnowami. : ‘Very Deep Convolutional Networks for Skin Lesion Classification’. J. King Abdulaziz Univ. Eng. Sci. Vol. 30, pp. 43-54, 2019. 10.4197/Eng. 30-2.5
XI. M. Hu, Y.Li, Y, and X. Yang. : ‘Skinsam: Empowering skin cancer segmentation with segment anything model’. arXiv preprint arXiv: 2304.13973, 2023. 10.48550/arXiv.2304.13973
XII. M. Llamas-Velasco, T. Mentzel, E. Ovejero-Merino, E. M. Teresa Fernández-Figueras, and H. Kutzner. : ‘CD64 staining in dermatofibroma: A sensitive marker raising the question of the cell differentiation lineage of this neoplasm’. Journal of Molecular Pathology. Vol. 3(4), pp. 190-195, 2022. 10.3390/jmp3040016
XIII. N. Ceylan, S. Kaçar, E. Güney, and C. Bayilmiş. : ‘Detection of Grinding Burn Fault in Bearings by Squeeze Net’. 30th Signal Processing and Communications Applications Conference (SIU). IEEE. pp. 1-4, 2022, May. 10.1109/SIU55565.2022.9864895
XIV. N. Kumar and T. Sandhan. : ‘Alternating Sequential and Residual Networks for Skin Cancer Detection from Biomedical Images’. National Conference on Communications (NCC). IEEE. pp. 1-5, 2023, February. 10.1109/NCC56989.2023.10068074
XV. N. Trivedi, V. Gautam, A. Anand, H. Aljahdali, S. Villar, D. Anand, and S. Kadry,: ‘Early detection and classification of tomato leaf disease using high-performance deep neural network’. Sensors. Vol. 21(23), 7987, 2021. 10.3390/s21237987
XVI. P. Dhiman, A. Kaur, Y. Hamid,E. Alabdulkreem, H. Elmannai, and N. Ababneh. : ‘Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing’. Sustainability. Vol. 15(5), 4576, 2023. 10.3390/su15054576
XVII. P. Dhiman, V. Kukreja, and A.Kaur. : ‘Citrus fruits classification and evaluation using deep convolution neural networks: an input layer resizing approach’. 2021 9th International Conference on Reliability, Infocom Technologies and Optimization, (Trends and Future Directions) (ICRITO). IEEE. pp. 1-4, 2021, September. 10.1109/ICRITO51393.2021.9596357
XVIII. R. Jain, S. Dubey, and G. Singhvi. : ‘The Hedgehog pathway and its inhibitors: Emerging therapeutic approaches for basal cell carcinoma’. Drug discovery today. Vol. 27(4), pp. 1176-1183, 2022. 10.1016/j.drudis.2021.12.005
XIX. S. Aggarwal,S. Gupta, A. Alhudhaif, D. Koundal, R. Gupta, and K. Polat. : ‘Automated COVID‐19 detection in chest X‐ray images using fine‐tuned deep learning architectures’. Expert Systems. Vol. 39(3), e12749, 2022. 10.1111/exsy.12749
XX. T. Mazhar, I. Haq, A. Ditta, S. Mohsan, F. Rehman, I. Zafar, and L. Goh, : ‘The role of machine learning and deep learning approaches for the detection of skin cancer’. Healthcare. MDPI. Vol. 11(3), 415, 2023, February. 10.3390/healthcare11030415
XXI. V. Anand, S. Gupta, A. Altameem, S. Nayak, R. Poonia, and A. Saudagar. : ‘An enhanced transfer learning based classification for diagnosis of skin cancer’. Diagnostics. Vol. 12(7), 1628, 2022. 10.3390/diagnostics12071628
XXII. V. Anand, S. Gupta, D. Koundal, S. Mahajan, A. Pandit, and A. Zaguia. ‘Deep learning based automated diagnosis of skin diseases using dermoscopy’. Computers, Materials & Continua. Vol. 71(2), pp. 3145-3160, 2022. 10.32604/cmc.2022.022788
XXIII. W. Gouda, N. Sama, G. Al-Waakid, M. Humayun, and N. Jhanjhi. : ‘Detection of skin cancer based on skin lesion images using deep learning’. Healthcare. MDPI. Vol. 10(7), 1183, 2022, June. DOI:10.3390/healthcare10071183.
XXIV. Y. Dahdouh, A. Anouar Boudhir, and M. Ben Ahmed. : ‘A New Approach using Deep Learning and Reinforcement Learning in HealthCare: Skin Cancer Classification’. International journal of electrical and computer engineering systems. Vol. 14(5), pp. 557-564, 2023. 10.32985/ijeces.14.5.7

View | Download