Research Article Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images Shilpa Rani, 1,2 Deepika Ghai, 3 Sandeep Kumar , 4 MVV Prasad Kantipudi , 5 Amal H. Alharbi, 6 and Mohammad Aman Ullah 7 1 Department of CSE, Lovely Professional University, Punjab, India 2 Department of CSE, Neil Gogte Institute of Technology, Hyderabad, Telangana, India 3 Department of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India 4 Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India 5 Department of E&TC, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India 6 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 7 Department of Computer Science and Engineering, International Islamic University Chittagong, Chittagong, Bangladesh Correspondence should be addressed to Mohammad Aman Ullah; aman_cse@iiuc.ac.bd Received 16 March 2022; Revised 5 April 2022; Accepted 9 April 2022; Published 20 May 2022 Academic Editor: Vijay Kumar Copyright © 2022 Shilpa Rani 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. In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. is led to the development of a computer-specific pattern recognition method in this study for identifying objects in medical images such as brain tumors. Initially, an adaptive median filter is used to remove the noise from MRI images. ereafter, the contrast image enhancement technique is used to improve the quality of the image. To evaluate the wireframe model, the cellular logic array processing (CLAP)-based algorithm is then applied to images. e basic patterns of three-dimensional (3D) images are then identified from the input image by scanning the whole image. e frequency of these patterns is also used for object classification. A deep neural network is then utilized for the classification of brain tumor. In the proposed model, the syntactic pattern recognition technique is used to find the feature vector and 3D AlexNet is used for brain tumor classification. To evaluate the performance of the proposed work, three benchmark brain tumor datasets are used, i.e., Figshare, Brain MRI Kaggle, and Medical MRI datasets and BraTS 2019 dataset. e comparative analyses reveal that the proposed brain tumor classification model achieves significantly better performance than the existing models. 1. Introduction Object recognition is an open-ended problem in the field of computer vision and medical image processing. For any object to recognize, a human being does not need to do much since we have multiple processing systems with billions of neurons: the brain. We require just a few milliseconds of visual stimulation to recognize an object, but the computer does not work like this because the computer needs more information for object recognition. A whole image can be given as an input but that will have computation limitations. erefore, a generalized representation of different objects in one domain can give different identities to the same domain’s objects. is generalized representation of objects can be further used for the identification of different diseases such as lung cancer and brain tumors. As image processing tech- niques play an essential role in the diagnosis and monitoring of patients, the biomedical field has gained much attention from researchers, and many advance artificial intelligence- based techniques are available for the early detection of the disease. However, due to the unpredictable nature of cancer, it still needs more advanced techniques. Brain cancer is a life-threatening disease, and approxi- mately every year, 80000 new cases are reported [1, 2]. e treatment of brain cancer always depends on the early and accurate detection of the tumor, and detection always Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 7882924, 19 pages https://doi.org/10.1155/2022/7882924