Affiliation:
1. Department of Electronics & Communication Engineering Sree Chitra Thirunal College of Engineering Thiruvananthapuram Kerala India
2. APJ Abdul Kalam Technological University Kerala India
3. College of Engineering Kallooppara Pathanamthitta Kerala India
Abstract
AbstractAutomated, as well as accurate classification with breast cancer histological images, was crucial for medical applications because of detecting malignant tumors via histopathological images. In this work create a Fourier ptychographic (FP) and deep learning using breast cancer histopathological image classification. Here the FP method used in the process begins with such a random guess that builds a high‐resolution complex hologram, subsequently uses iterative retrieval using FP constraints to stitch around each other low‐resolution multi‐view means of production owned from either the hologram's high‐resolution hologram's elemental images captured via integral imaging. Next, the feature extraction process includes entropy, geometrical features, and textural features. The entropy‐based normalization is used to optimize the features. Finally, it attains the classification process of the proposed ENDNN classifies the breast cancer images into normal or abnormal. The experimental outcomes demonstrate that our presented technique overtakes the traditional techniques.
Subject
General Physics and Astronomy,General Engineering,General Biochemistry, Genetics and Molecular Biology,General Materials Science,General Chemistry
Cited by
1 articles.
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