Skin Cancer Detection using CNN Algorithm
Roopa Sri Paladugu1, Anusha Immadisetty2, M. Ramesh3
1Roopa Sri Paladugu*, Department of IT, V.R. Siddhartha Engineering College, Vijayawada, India.
2Anusha Immadisetty, Department of IT, V.R. Siddhartha Engineering College, Vijayawada, India.
3Dr. M. Ramesh, Department of IT, V.R. Siddhartha Engineering College, Vijayawada, India.
Manuscript received on July 02, 2020. | Revised Manuscript received on July 10, 2020. | Manuscript published on August 30, 2020. | PP: 45-49 | Volume-9 Issue-6, August 2020. | Retrieval Number: E1079069520/2020©BEIESP | DOI: E1079069520/2020©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is well known as HAM10000 dataset, it consists of massive amounts of information regarding several dermatoscopic images of most trivial pigmented lesions of skin which are collected from different sufferers. Once the dataset is collected, cleaned, it is split into training and testing data sets. We used CNN to build our model and using the training data we trained the model , later using the testing data we tested the model. Once the model is implemented over the testing data, plots are made in order to analyze the relation between the echos and loss function. It is also used to analyse accuracy and echos for both training and testing data.
Keywords: Sequential model, keras, skin cancer, cnn