Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images

Abstract

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. This 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. Thereafter, 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. The basic patterns of three-dimensional (3D) images are then identified from the input image by scanning the whole image. The 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. The comparative analyses reveal that the proposed brain tumor classification model achieves significantly better performance than the existing models.

Exploring the Depths of Visual Understanding: A Comprehensive Review on Real-Time Object of Interest Detection Techniques

“…Aapplied fine-tuned Alex.net with a CNN model trained with the Cifar-10 version-2019 dataset for object detection and recorded 98% classification accuracy [78]. Also trained CNN with four brain tumor datasets which are Figshare, Brain MRI Kaggle, Medical MRI datasets and BraTS 2019 datasets to generate a model used to fine-tune Alex.Net as a 3D model for the classification of brain tumours [79]. The average performance for accuracy is 99%, mean Average Precision (mAP) reported 99%, sensitivity 87% and detection time of 1300ms.…”

“…From the literature reviewed, it was observed that many studies have applied diverse deep learning techniques in addressing classification problems, among the approaches is Alex.Net which has been trained with several datasets and evaluated. The results despite the success revealed certain gaps such as occlusion in [78], the need for improved sensitivity in [79], and delay when applied for real-time classification in [70, 75 and 80]. In the case of Mobile-Net, which has also been tested on several datasets, overall results need improvement in [15,16], lack of instructiveness was identified in [68, 69 and 84] as the gap.…”

Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images

“…2. Three-dimensional AlexNet (Rani et al, 2022;Chen et al, 2021;Menon et al, 2020) architecture used for deep learning. The input was a single MRI volume, and the output generated an inference of two categories: clean or artifact.…”

“…Yet, to the best of our knowledge, uncertainty metrics have not been reported in studies that aim to detect MRI artifacts for large neuroimaging databases. Here we implemented a 3D AlexNet (Rani et al, 2022;Chen et al, 2021;Menon et al, 2020), to first apply a deep neural network algorithm and reproduce the high performance previously obtained with a small balanced dataset. We then develop a method to utilize this algorithm for analyzing large, imbalanced neuroimaging databases while maintaining the detection performance.…”

Pattern Recognition in Medical Images Through Innovative Edge Detection with Robert's Method

“…Pattern recognition in medical images is one of the important aspects in the world of modern medicine (Hauser, 2022). In computer vision and medical image processing, object recognition is a major concern today (Rani et al, 2022). Medical images such as X-rays, CT scans, MRI, and ultrasound are widely used for disease diagnosis, treatment planning, and patient monitoring.…”