Intelligent Fatigue Detection by Using ACS and by Avoiding False Alarms of Fatigue Detection

Swathi, A.; Rani, Shilpa

doi:10.1007/978-981-13-7082-3_27

Cited by 6 publications

(1 citation statement)

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“…erefore, the 3D AlexNet architecture is used for the classification of tumor type. is research's primary motivation was to give a new insight into the structural pattern recognition technique because structural pattern recognition techniques can deal with complex medical images [25][26][27][28][29][30]. Keeping this into mind, we targeted our few objectives, which are as follows: is study is further divided into four sections to achieve the objectives mentioned above.…”

Section: Introductionmentioning

confidence: 99%

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

Rani

Ghai

Kumar

et al. 2022

Computational Intelligence and Neuroscience

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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.

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