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The Smart Detection and Analysis on Skin Tumor Disease Using Bio Imaging Deep Learning Algorithm
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Skin cancer is one of the most common and devastating forms of cancer. It is estimated that one out of every five individuals will develop skin cancer at some point in their lifetime. Early detection and treatment are essential for successful outcomes, and thus, developing automated and accurate detection methods for skin tumors is of great interest. In this paper, a bio-imaging based deep learning algorithm, have made it possible to accurately detect and analyze skin tumor diseases. This algorithm use complex neural network architectures to automatically identify and classify skin lesions from medical images. These methods can significantly help reduce the workload of dermatologists and improve the accuracy and speed of skin cancer detection. This study reviews the current research on automated skin tumor detection and analysis using deep learning algorithms, and presents some of the most promising directions for further investigation.
Keywords
Skin Cancer, Early Detection, Treatment, Bio-Imaging, Deep Learning, Accuracy.
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- J. Xie and C. Wang, “Using Support Vector Machines with a Novel Hybrid Feature Selection Method for Diagnosis of Erythemato-Squamous Diseases”, Expert Systems with Applications, Vol. 38, No. 5, pp. 5809-5815, 2011.
- R.O. Duda, P.E. Hart and D.G. Stock, “Pattern Classification”, Wiley-Interscience, 2001.
- D. Giveki, H. Salimi, A.A. Bitaraf and Y. Khademian, “Detection of Erythemato-Squamous Diseases using ARCatfish BPSO-KSVM”, Signal and Image Processing, Vol. 2, No. 4, pp. 57-72, 2011.
- G. Sekar, “NMLA: The Smart Detection of Motor Neuron Disease and Analyze the Health Impacts with Neuro Machine Learning Model”, NeuroQuantology, Vol. 20, No. 8, pp. 892-899, 2022.
- R.K. Nayak and D.K. Anguraj, “A Novel Strategy for Prediction of Cellular Cholesterol Signature Motif from G Protein-Coupled Receptors based on Rough Set and FCM Algorithm”, Proceedings of International Conference on Computing Methodologies and Communication, pp. 285-289, 2020.
- T.R. Sivapriya and V. Saravanan, “Automatic Brain MRI Mining using Support Vector Machine and Decision Tree”, CiiT International Journal of Artificial Intelligent Systems and Machine Learning, Vol. 3, No. 2, pp. 109-116, 2011.
- L. Mary Florida and V. Paul, “Modelling of Maximal Connectivity Pattern in Human Brain Networks”, Neuroquantology, Vol. 20, No. 6, pp. 4410-4421, 2022.
- C. Sivakumar and A. Shankar, “The Speech-Language Processing Model for Managing the Neuro-Muscle Disorder Patients by using Deep Learning”, Neuroquantology, Vol. 20, No. 8, pp. 918-925, 2022.
- R. Manikandan, S.S. Priscila and M. Ramkumar, “Sequential Pattern Mining on Chemical Bonding Database in the Bioinformatics Field”, Proceedings of International Conference on AIP Publishing, pp. 1-13, 2022.
- M. Ramkumar and A. Alene, “Healthcare Biclustering-based Prediction on Gene Expression Dataset”, BioMed Research International, Vol. 2022, pp. 1-14, 2022.
- A. Esteva and S. Thrun, “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks”, Nature, Vol. 542, pp. 115-118, 2017.
- B. Harangi, A. Baran and A. Hajdu, “Classification of Skin Lesions using an Ensemble of Deep Neural Networks”, Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2575-2578, 2018.
- P. Saxena and A. Sharma, “The Deep DNA Machine Learning Model to Classify the Tumor Genome of Patients with Tumor Sequencing”, International Journal of Health Sciences, Vol. 6, No. 5, pp. 9364-9375, 2022.
- N.C. Codella, S. Dusza and A. Halpern, “Skin Lesion Analysis Toward Melanoma Detection”, Proceedings of International Symposium on Biomedical Imaging, pp. 168-172, 2018.
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