Breast Cancer Detection Using Deep Learning - ahmed Abed Maeedi; Dalal Abdulmohsin Hammood; Shatha Mezher Hasan (Iraqi Journal for Computers and Informatics)
This research aims to develop an image classification model by integrating long short-term memory (LSTM) with a convolutional neural network (CNN). LSTM, which is a type of neural network, can retain and retrieve long-term dependencies and improves the feature extraction capabilities of CNN when used in a multi-layer setting. The proposed approach outperforms typical CNN classifiers in image classification. The model’s high accuracy is due to the data passing through two stages and multiple layers: first the LSTM layer, followed by the CNN layer for accurate classification. Convolutional and recurrent neural networks are combined in the recommended model, which demonstrates exceptional performance on various classification tasks. The model achieved a training accuracy of 0.9899 and testing accuracy of 0.9463 using real data, which indicates its success and applicability compared with other models.