Research Article A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection Shahab Ahmad, 1 Tahir Ullah, 2 Ijaz Ahmad , 3 Abdulkarem AL-Sharabi, 4 Kalim Ullah, 5 Rehan Ali Khan , 6 Saim Rasheed, 7 Inam Ullah , 8 Md. Nasir Uddin, 9 and Md. Sadek Ali 9 1 School of Management Science and Engineering, Chongqing University of Post and Telecommunication, Chongqing 400065, China 2 Department of Electronics and Information Engineering, Xian Jiaotong University, Xian, China 3 Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China 4 Dalian Medical College and University, Dalian 116044, China 5 Department of Zoology, Kohat University of Science and Technology, Kohat 26000, Pakistan 6 Department of Electrical Engineering, University of Science and Technology, Bannu 28100, Pakistan 7 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University Jeddah, Saudi Arabia 8 College of Internet of ings (IoT) Engineering, Hohai University (HHU), Changzhou Campus, Nanjing 213022, China 9 Communication Research Laboratory, Department of Information and Communication Technology, Islamic University, Kushtia 7003, Bangladesh Correspondence should be addressed to Md. Sadek Ali; sadek@ice.iu.ac.bd Received 20 February 2022; Revised 28 April 2022; Accepted 1 June 2022; Published 24 June 2022 Academic Editor: Shibli Nisar Copyright © 2022 Shahab Ahmad et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body’s normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with treatment in the early stage. It must also divide cancer patients into high- and low-risk groups. While realizing efficient detection of cancer is frequently a time-taking and exhausting task with the high possibility of pathologist errors and previous studies employed data mining and machine learning (ML) techniques to identify cancer, these strategies rely on handcrafted feature extraction techniques that result in incorrect classification. On the contrary, deep learning (DL) is robust in feature extraction and has recently been widely used for classification and detection purposes. is research implemented a novel hybrid AlexNet-gated recurrent unit (AlexNet-GRU) model for the lymph node (LN) breast cancer detection and classification. We have used a well-known Kaggle (PCam) data set to classify LN cancer samples. is study is tested and compared among three models: convolutional neural network GRU (CNN-GRU), CNN long short-term memory (CNN-LSTM), and the proposed AlexNet-GRU. e experimental results indicated that the performance metrics accuracy, precision, sensitivity, and specificity (99.50%, 98.10%, 98.90%, and 97.50) of the proposed model can reduce the pathologist errors that occur during the diagnosis process of incorrect classification and significantly better performance than CNN- GRU and CNN-LSTM models. e proposed model is compared with other recent ML/DL algorithms to analyze the model’s efficiency, which reveals that the proposed AlexNet-GRU model is computationally efficient. Also, the proposed model presents its superiority over state-of-the-art methods for LN breast cancer detection and classification. 1. Introduction Cancer is a group of uncontrolled development cells in the body, which may spread to any organ abruptly [1, 2]. ere are many distinct kinds of cancer, but lung cancer, breast cancer (BC), and skin cancer are the most prevalent. According to the World Health Organization (WHO) re- ports, the cancer death ratio is up to 9.2 million in lung cancer and 1.7 million in skin cancer, while breast cancer has caused 627,000 deaths [3, 4]. Breast cancer survival is Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 8141530, 14 pages https://doi.org/10.1155/2022/8141530