Open Access

ARTICLE

Classification of Human Protein in Multiple Cells Microscopy Images Using CNN

Lina Al-joudi, Muhammad Arif^*

Department of Computer Science AI (CS-AI), Umm Al Qura University (UQU), Makkah, 24231, Saudi Arabia

* Corresponding Author: Muhammad Arif. Email:

Computers, Materials & Continua 2023, 76(2), 1763-1780. https://doi.org/10.32604/cmc.2023.039413

Received 28 January 2023; Accepted 24 May 2023; Issue published 30 August 2023

Abstract

The subcellular localization of human proteins is vital for understanding the structure of human cells. Proteins play a significant role within human cells, as many different groups of proteins are located in a specific location to perform a particular function. Understanding these functions will help in discovering many diseases and developing their treatments. The importance of imaging analysis techniques, specifically in proteomics research, is becoming more prevalent. Despite recent advances in deep learning techniques for analyzing microscopy images, classification models have faced critical challenges in achieving high performance. Most protein subcellular images have a significant class imbalance. We use oversampling and under sampling techniques in this research to overcome this issue. We have used a Convolutional Neural Network (CNN) model called GapNet-PL for the multi-label classification task on the Human Protein Atlas Classification (HPA) Dataset. Authors have found that the Parametric Rectified Linear Unit (PreLU) activation function is better than the Scaled Exponential Linear Unit (SeLU) activation function in the GapNet-PL model in most classification metrics. The results showed that the GapNet-PL model with the PReLU activation function achieved an area under the ROC curve (AUC) equal to 0.896, an F1 score of 0.541, and a recall of 0.473.

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