Prediction and Classification of Ovarian Cancer using Enhanced Deep Convolutional Neural Network

Prediction and Classification of Ovarian Cancer using Enhanced Deep Convolutional Neural Network
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Kokila. R. Kasture, Dharmaveer Choudhari, Pravin N. Matte
https://doi.org/10.14445/22315381/IJETT-V70I3P235

How to Cite?

Kokila. R. Kasture, Dr. Dharmaveer Choudhari, Pravin N. Matte, "Prediction and Classification of Ovarian Cancer using Enhanced Deep Convolutional Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 310-318, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P235

Abstract
For the prediction and classification of Ovarian cancer`s four subtypes using histopathological pictures, this article uses a deep convolutional neural network (DCNN). With a dismal survival rate, Ovarian Cancer is the fifth most common and most aggressive kind of gynecologic cancer. Serous, mucinous, endometroid, and clear cell are the four major subtypes of ovarian epithelial cancer. A new trend in medical picture analysis is the use of computers to assist in the detection of various diseases such as cancer, brain tumors, seizures, and Alzheimer`s. An improved DCNN-based architecture for detecting benign and malignant cells has been developed and implemented in this paper, as shown in the figure. A subtype can be added if it is malignant. The researchers used 500 histopathological pictures from The Cancer Genome Atlas (TCGA-OV) collection, which had been made publically available, to create a total of 24,742 new images. By augmenting the photos used as training data, the proposed classification model, called KK-Net, went from 75% to 91% accuracy. This model`s performance was evaluated using the AUC-ROC curve (Area under the Curve - Receiver Operating Characteristics) statistical analysis approach. An AUC-ROC curve value of 95 percent was reached on average. On top of that, we used AlexNet, VGG-16, VGG-19, and GoogleNet to test the suggested model`s performance against the state-of-the-art approaches. Pathologists will be able to detect ovarian cancer in its earliest stages thanks to this newly established unique design, which can serve as a standard for predicting and classifying the disease.

Keywords
Artificial Intelligence, Machine learning, Predictive methods, Supervised learnings, Image processing.

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