International Conference on Computer and Knowledge Engineering

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Histopathology Image-Based Cancer Classification Utilizing Transfer Learning Approach

Authors :

Amir Meydani¹ Alireza Meidani² Ali Ramezani³ Maryam Shabani⁴ Mohammad Mehdi Kazeminasab⁵ Shahriar Shahablavasani⁶

1- Department of Electrical, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran 2- School of Electrical and Computer Engineering University of Tehran 3- Department of Electrical, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran 4- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran 5- School of Electrical and Computer Engineering University of Tehran Tehran, Iran 6- Department of Electrical, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Keywords :

Transfer Learning (TL)،Histopathology Images (HIs)،DenseNet121،Accuracy Detection،Deep Learning (DL)،Machine Learning (ML)،Convolutional Neural Network (CNN)

Abstract :

The primary objective of medicine and technology is to provide services capable of identifying and treating patients based on their particular conditions. The accuracy of disease diagnosis is of paramount importance to this endeavor. Cancer is a major cause of death on a global scale, with prognosis, prevention, and prompt intervention offering the possibility of complete patient remission. In this Python-based experiment, we investigate the viability of transfer learning for lymph node diagnosis in histopathology images, which are used to fine-tune a DenseNet121-based pre-trained model. The results indicate that fine-tuning the DenseNet121 model is more effective than training the model from start. Due to variations in the network's initial weight distribution, the network's average accuracy is 96%. Following the implementation and completion of ten training epochs, the average overall accuracy reaches a maximum of 98%, with less than 10% error accuracy.