A Study on Deep Learning Methods for Skin Disease Classification

  • N.Vanitha Assistant Professor, Department of Information Technology, Dr. N.G.P. Arts and Science College, Coimbatore, INDIA
  • M.Geetha Student, Department of Information Technology, Dr. N.G.P. Arts and Science College, Coimbatore, INDIA
Keywords: Disease of the Skin, Deep Learning, Types, Significance


Dermatological disorders are one among the foremost widespread diseases within the world. Despite being common its diagnosis is extremely difficult due to its complexities of skin tone, color, presence of hair. This paper provides an approach to use various computer vision-based techniques (deep learning) to automatically predict the varied sorts of skin diseases. The system makes use of deep learning technology to coach itself with the varied skin images. the most objective of this technique is to realize maximum accuracy of disease of the skin prediction. The people health quite the other diseases. Skin diseases are mostly caused by mycosis, bacteria, allergy, or viruses, etc. The lasers advancement and Photonics based medical technology is employed in diagnosis of the skin diseases quickly and accurately. The medical equipment for such diagnosis is restricted and costliest. So, Deep learning techniques helps in detection of disease of the skin at an initial stage. The feature extraction plays a key role in classification of skin diseases. The usage of Deep Learning algorithms has reduced the necessity for human labor, like manual feature extraction and data reconstruction for classification purpose.


Download data is not yet available.


R.D.Delbridge, L.J.Valente, & A.Strasser. (2012). The role of the apoptotic machinery in tumor suppression. Cold Spring Harbor Perspect. Biol., 4(11), Art. No. A008789.

M. Thorn, F. Ponte, R. Bergstrom, P. Sparen, & H.-O. Adami. (1994). Clinical and histopathologic predictors of survival in patients with malignant melanoma: A population-based study in Sweden. JNCI J. Nat. Cancer Inst., 86(10), 761–769.

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, & S.Thrun. (2017 Feb). Dermatologist level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.

H.Kittler, H.Pehamberger, K.Wolff, & M.Binder. (2002). Diagnostic accuracy of dermoscopy. Lancet Oncol., 3(3), 159–165.

P. Carli, E. Quercioli, S. Sestini, M. Stante, L. Ricci, G. Brunasso, & V. DE Giorgi. (2003) May). Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology. Brit. J. Dermatology, 148(5), 981–984.

L.M. Abbott & S.D.Smith (2018 Aug). Smartphone apps for skin cancer diagnosis: Implications for patients and practitioners. Australas. J. Dermatology, 59(3), 168–170.

M. E. Celebi, H. A. Kingravi, B. Uddin, H. Iyatomi, Y. A. Aslandogan, W. V. Stoecker, & R. H. Moss. (2007 Sep). A methodological approach to the classification of dermoscopy images. Computerized Med. Imag. Graph., 31(6), 362–373.

G. Defazio et al. (2015). Development and validation of a clinical scale for rating the severity of blepharospasm. Movement Disorders, 30(4), 525-530.

D. Sanders, E. W. Massey, & E. Buckley. (1986). Botulinum toxin for blepharospasm: single-fiber EMG studies. Neurology, 36(4), 545-547.

A. Berardelli, J. Rothwell, B. Day, & C. Marsden. (1985). Pathophysiology of blepharospasm and oromandibular dystonia. Brain, 108(3), 593-608.

K.-L. E. Hon, M.-C. A. Lam, T.-F. Leung, C.-M. Chow, E. Wong, & A. K. Leung. (2007). Assessing itch in children with atopic dermatitis treated with tacrolimus: Objective versus subjective assessment. Adv. Therapy, 24(1), 23–28.

C. Bringhurst, K. Waterston, O. Schofield, K. Benjamin, & J. L. Rees. (2004). Measurement of itchusingactigraphyinpediatric and adult populations. J. Amer. Acad. Dermatol., 51(6), 893–898.

K. Benjamin, K. Waterston, M. Russell, O. Schofield, B. Diffey, & J. L. Rees. (2004). The development of an objective method for measuring scratch in children with atopic dermatitis suitable for clinical use. J. Amer. Acad. Dermatol., 50(1), 33–40.

How to Cite
N.Vanitha, & M.Geetha. (2021). A Study on Deep Learning Methods for Skin Disease Classification. International Journal of Engineering and Management Research, 11(2), 48-52. https://doi.org/10.31033/ijemr.11.2.7