Review of Deep Learning and Bioinformatics in Breast Cancer
Salam Musa, Zeinab Adam Mustafa, Ahmed Zein · Journal of Clinical Engineering · 2025
Cancer is one of the commonest causes of patient death in the clinic; unfortunately, breast cancer is the most common cancer causing death in women around the world. Deep learning (DL) is a powerful tool in the area of data and imaging processing. Deep learning has a vital role in cancer diagnosis, precision medicine, predictive forecasting, sequence analysis, and bioinformatics field. Deep learning can also overcome the limitations of earlier shallow networks and also provide new approaches that can lead to more accurate, fast results and efficient models for data analysis and classification. Recurrent neural network and convolutional neural network have increasingly received attention in bioinformatics area. Several recurrent neural network and convolutional neural network methods for classification and analysis have been studied. This article offers an elaborate study of different machine learning and DL techniques used in the analysis, prediction, and classification of breast cancer. Many researchers have put their efforts into breast cancer diagnoses and prediction; every technique has different accuracy rates, and it varies for different situations, tools, and datasets being used. The main purpose of this review is to focus and analyze DL to find out the most appropriate method that will support the large dataset with good accuracy of prediction.