An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer
benign; classification; convolutional neural network; data augmentation; Kaggle; malignant; skin cancer; VGG16
Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can detect skin cancer in its earliest stages is necessary. For the diagnosis of skin cancer, various researchers have performed tasks using deep learning and transfer learning models. However, the existing literature is limited in terms of its accuracy and its troublesome and time-consuming process. As a result, it is critical to design an automatic system that can deliver a fast judgment and considerably reduce mistakes in diagnosis. In this work, a deep learning-based model has been designed for the identification of skin cancer at benign and malignant stages using the concept of transfer learning approach. For this, a pre-trained VGG16 model is improved by adding one flatten layer, two dense layers with activation function (LeakyReLU) and another dense layer with activation function (sigmoid) to enhance the accuracy of this model. This proposed model is evaluated on a dataset obtained from Kaggle. The techniques of data augmentation are applied in order to enhance the random-ness among the input dataset for model stability. The proposed model has been validated by considering several useful hyper parameters such as different batch sizes of 8, 16, 32, 64, and 128; different epochs and optimizers. The proposed model is working best with an overall accuracy of 89.09% on 128 batch size with the Adam optimizer and 10 epochs and outperforms state-of-the-art techniques. This model will help dermatologists in the early diagnosis of skin cancers. 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Anand V.; Gupta S.; Altameem A.; Nayak S.R.; Poonia R.C.; Saudagar A.K.J.
Multidisciplinary Digital Publishing Institute (MDPI)
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.3390/diagnostics12071628 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133849397&doi=10.3390%2fdiagnostics12071628&partnerID=40&md5=6e1f7b12eb723fbd8694fe1b30481367
Anand V., Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, 140401, India; Gupta S., Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, 140401, India; Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11533, Saudi Arabia; Nayak S.R., Amity School of Engineering and Technology, Amity University Uttar Pradesh, Uttar Pradesh, Noida, 201301, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia