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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Krishnasamy, Valarmathi
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Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Performance analysis of breast cancer histopathology image classification using transfer learning models Ramasamy, Meena Prakash; Subburaj, Thayammal; Krishnasamy, Valarmathi; Mannarsamy, Vimala
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp6006-6015

Abstract

Convolutional neural networks (CNN) which are deep learning-based methods are being currently successfully deployed and have gained much popularity in medical image analysis. CNN can handle enormous amounts of medical data which makes it possible for accurate detection and classification of breast cancer from histopathological images. In the proposed method, we have implemented transfer learning-based classification of breast cancer histopathological images using DenseNet121, DenseNet201, VGG16, VGG19, InceptionV3, and MobileNetV2 and made a performance analysis of the different models on the publicly available dataset of BreakHis. These networks were pre-trained on the ImageNet database and initialized with weights which are fine-tuned by training with input histopathological images. These models are trained with images of the BreakHis dataset with multiple image magnifications. From the comparative study of these pre-trained models on histopathology images, it is inferred that DenseNet121 achieves the highest breast cancer classification accuracy of 0.965 compared to other models and contemporary methods.
A unique YOLO-based gated attention deep convolution network-Lichtenberg optimization algorithm model for a precise breast cancer segmentation and classification Rathinam, Vinoth; Rajendran, Sasireka; Krishnasamy, Valarmathi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1670-1685

Abstract

A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care.
Co-Authors Arunagiri, Ramathilagam Mannarsamy, Vimala Rajendran, Sasireka Ramasamy, Meena Prakash Rathinam, Vinoth Subburaj, Thayammal