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Research Article(ISSN: 2644-1306)

Clinical Image Analysis for Detection of Skin Cancer Using Convolution Neural Networks

Volume 1 - Issue 3

Anand Pandey, Aman Sharma and S P Syed Ibrahim*

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    • School of Computing Sciences and Engineering, VIT Chennai Campus, India

    *Corresponding author: SP Syed Ibrahim, School of Computing Sciences and Engineering, VIT Chennai Campus, India

Received: April 29, 2019;   Published:May 06, 2019

DOI: 10.32474/TRSD.2018.01.000111

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Abstract

Humankind, since the growth of its civilization past the industrial revolution, has been a witness to an exponential rise in the number of causes and fatalities of unprecedented deaths. Despite having a humongous growth of technology since the late 80’s, researchers and doctors have not been able to fully develop cutting-edge technology that could play a crucial role in reducing life-threatening cases of diseases around the world. Diagnosis of the disease requires a high level of precision and expertise in a variety of visual aspects. Technology in the field of Medical Science has been successful in achieving unprecedented heights and has played a major role ranging from medical assistance to automation in surgeries. Machine Learning technologies have dominated by having a positive endeavor towards detecting life-long diseases at an early stage. Deep Learning has played an important role in this domain to give doctors and research scholars unimaginable assistance in predicting cancerous cases. This paper suggests an implementation of a CNN model being malignant or benign. Since the dataset was quite imbalanced, the proposed method applies Gaussian filtering over the sample images and then processed it. We have proposed an architecture named “Adnet” that has a particular recursive flow of layer networks which provides better accuracy than the pre-existing models Figure 1.

Keywords: Cancer Classification; Deep Learning; CNN; Inception; VGG

Abstract| Introduction| Conclusion and Future Work| References|

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