Paper Title :Comparative Analysis of Deep Learning Techniques for Breast Tumor Classification on Small-Balanced and Large-Unbalanced Datasets
Author :Najme Zehra Naqvi, Amisha Jangra, Vittesha Gupta, Sanya Tiwari, Mahima Narang
Article Citation :Najme Zehra Naqvi ,Amisha Jangra ,Vittesha Gupta ,Sanya Tiwari ,Mahima Narang ,
(2023 ) " Comparative Analysis of Deep Learning Techniques for Breast Tumor Classification on Small-Balanced and Large-Unbalanced Datasets " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 73-79,
Volume-11,Issue-6
Abstract : Effective screening of diseases is necessary in order to control them and prevent them from spreading. As
computer-aided diagnosis technology continues to evolve, there is a discernible trend towards improving the accuracy of
diagnoses. There are multiple requirements that biomedical image datasets must meet in order to be useful for implementing
deep learning techniques for effective screening. However, biomedical image datasets are often small and limited. In this
work, multiple research papers are reviewed and it is concluded that there is a lack of understanding regarding the
effectiveness of deep learning techniques for such datasets. As a result, the accuracy of the implemented model suffers and
biomedical images are not classified correctly. While there has been some research on this topic, it is still not clear whether
deep learning techniques perform better or worse on small-balanced versus large-unbalanced datasets. To resolve this
problem, we developed two different datasets from the Breast Ultrasound Images (BUSI) database - small and balanced
containing equal number of images for all the classes and large but unbalanced using data augmentation. Then, a variety of
deep learning techniques are used with for binary classification of ultrasound images of breasts tumours into malignant and
benign classes. A comparative analysis of the implemented techniques is performed on both the datasets, small-balanced and
the augmented dataset (large-unbalanced), by noting metrics accuracy score for each technique. Experimental findings have
indicated that a greater accuracy is obtained for the small-balanced dataset using Mobile Net technique with an accuracy
score of 87.5% as compared to 75% obtained using Dense Net method on the augmented-unbalanced dataset.
Keywords - Breast Tumor Classification, Deep Learning, Image Classification
Type : Research paper
Published : Volume-11,Issue-6
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-19833
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Copyright: © Institute of Research and Journals
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Published on 2023-10-13 |
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