Breast cancer histological images nuclei segmentation and optimized classification with deep learning

Abbasi, Muhammad Inam and Khan, Fawad Salam and Khurram, Muhammad and Mohd, Mohd Norzali and Khan, Muhammad Danial (2022) Breast cancer histological images nuclei segmentation and optimized classification with deep learning. International Journal Of Electrical And Computer Engineering (IJECE), 12 (4). pp. 4099-4110. ISSN 2088-8708

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Abstract

Breast cancer incidences have grown worldwide during the previous few years. The histological images obtained from a biopsy of breast tissues are regarded as being the highest accurate approach to determine whether any cells exhibit symptoms of cancer. The visible position of nuclei inside the image is achieved through the use of instance segmentation, nevertheless, this work involves nucleus segmentation and features classification of the predicted nucleus for the achievement of best accuracy. The extracted features map using the feature pyramid network has been modified using segmenting objects by locations (SOLO) convolution with grasshopper optimization for multiclass classification. A breast cancer multi-classification technique based on a suggested deep learning algorithm was examined to achieve the accuracy of 99.2% using a huge database of ICIAR 2018, demonstrating the method’s efficacy in offering an important weapon for breast cancer multi-classification in a medical setting. The segmentation accuracy achieved is 88.46%

Item Type:	Article
Uncontrolled Keywords:	Breast cancer, Histological images, Mask regional convolutional network, Nuclei, Segmentation
Divisions:	Faculty of Electrical and Electronic Engineering Technology > Department of Electronic and Computer Engineering Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	06 Mar 2023 12:44
Last Modified:	06 Mar 2023 12:44
URI:	http://eprints.utem.edu.my/id/eprint/26393
Statistic Details:	View Download Statistic

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