Adaptive Jellyfish Search Optimization Trained Deep Learning for Breast Cancer Classification Using Histopathological Images


  • Vijaya P. Modern College of Business and Science



Breast cancer, Deep learning, Median filter, Histopathological images, Jelly fish search optimization


One of the most common cancers identified worldwide among women is breast cancer, which has become the foremost reason for death. Due to complexity of breast tissues, it is essential to accurately detect and classify breast cancer in medical imaging. In this paper, an optimization-based deep learning technique is created to detect breast cancer using histopathology pictures. The pre-processed images are sent to the segmentation procedure, where the Fuzzy Local Information C-Means (FLICM) technique is used to segment the blood cells. The segmentation process is followed by feature extraction, where various features, such as area, shape, diameter, and Speeded-Up Robust Features (SURF), are extracted from histopathological images. Lastly, Deep Q Network (DQN) trained using the developed Adaptive Jelly Fish Search Optimization (AJSO) algorithm is used to identify and classify benign and malignant breast cancer. The devised approach has good accuracy, TPR, and TNR values of 91.1%, 92.1%, and 92%, correspondingly.


Mohanakurup, V., ParambilGangadharan, S.M., Goel, P., Verma, D., Alshehri, S., Kashyap, R. and Malakhil, B., “Breast cancer detection on histopathological images using a composite dilated Backbone Network”, Computational Intelligence and Neuroscience, 2022.

Ahmad, N., Asghar, S. and Gillani, S.A., “Transfer learning-assisted multi-resolution breast cancer histopathological images classification”, The Visual Computer, vol.38, no.8, pp.2751-2770, 2022.

Zou, Y., Zhang, J., Huang, S. and Liu, B., “Breast cancer histopathological image classification using attention high‐order deep network”, International Journal of Imaging Systems and Technology, vol.32, no.1, pp.266-279, 2022.

Burcak, K.C., Baykan, O.K. and Uguz, H., “A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimization of the proposed model”, The Journal of Supercomputing, vol.77, pp.973-989, 2021.

Maheshan, C.M. and Prasanna Kumar, H., “Performance of image pre-processing filters for noise removal in transformer oil images at different temperatures”, SN Applied Sciences, vol. 2, pp.1-7, 2020.

Krinidis, S. and Chatzis, V., “A robust fuzzy local information C-means clustering algorithm”, IEEE transactions on image processing, vol.19, no.5, pp.1328-1337, 2010.

Bay, H., Ess, A., Tuytelaars, T. and Van Gool, L., “Speeded-up robust features (SURF)”, Computer vision and image understanding, vol.110, no.3, pp.346-359, 2008.

Sasaki, H., Horiuchi, T. and Kato, S., “A study on vision-based mobile robot learning by deep Q-network”, In Proceedings of 2017 56th annual conference of the society of instrument and control engineers of Japan (SICE), pp. 799-804, IEEE, 2017.

Chou, J.S. and Molla, A., “Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems”, Scientific Reports, vol. 12, no. 1, pp.19157, 2022.

Hu, C., Sun, X., Yuan, Z. and Wu, Y., “Classification of breast cancer histopathological image with deep residual learning”, International Journal of Imaging Systems and Technology, vol.31, no.3, pp.1583-1594, 2021.

Jiang, Y., Chen, L., Zhang, H. and Xiao, X., “Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module”,PloS one, vol.14, no.3, pp. e0214587, 2019.

He, K., Zhang, X., Ren, S. and Sun, J., “Deep residual learning for image recognition”, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778, 2016.

Figshare dataset is taken from,“”, accessed on March 2023.

Ghayumizadeh, H., Pakdelazar, O., Haddadnia, J., Rezai, R.G. And Mohammad, Z.M.,“Diagnosing breast cancer with the aid of fuzzy logic based on data mining of a genetic algorithm in infrared images”, 2012.

Shadi Momtahen, Maryam Momtahen, Ramani Ramaseshan, and Farid Golnaraghi, "A Machine Learning Approach: NIR Scattering Data Analysis for Breast Cancer Detection and Classification," In proceedings of the IEEE 1st Industrial Electronics Society Annual On-Line Conference (ONCON), Kharagpur, India, 2022.

Yanan Shao, Hoda S. Hashemi, Paula Gordon, Linda Warren, Jane Wang, Robert Rohling, and Septimiu Salcudean, "Breast Cancer Detection Using Multimodal Time Series Features From Ultrasound Shear Wave Absolute Vibro-Elastography," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 2, pp. 704 - 714, February 2022.

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