Santosh Kumar Tripathy

Early detection of Alzheimer's disease (AD) is critical due to its rising prevalence. AI-aided AD diagnosis has grown for decades. Most of these systems use deep learning using CNN. However, a few concerns must be addressed to identify AD: a. there is a lack of attention paid to spatial features; b. there is a lack of scale-invariant feature modelling; and c. the convolutional spatial attention block (C-SAB) mechanism is available in the literature, but it exploits limited feature sets from its input features to obtain a spatial attention map, which needs to be enhanced. The suggested model addresses these issues in two ways: through a backbone of multilayers of depth-separable CNN. Firstly, we propose an improved spatial convolution attention block (I-SAB) to generate an enhanced spatial attention map for the multilayer features of the backbone. The I-SAB, a modified version of the C-SAB, generates a spatial attention map by combining multiple cues from input feature maps. Such a map is forwarded to a multilayer of depth-separable CNN for further feature extraction and employs a skip connection to produce an enhanced spatial attention map. Second, we combine multilayer spatial attention features to make scale-invariant spatial attention features that can fix scale issues in MRI images. We demonstrate extensive experimentation and ablation studies using two open-source datasets, OASIS and AD-Dataset. The recommended model outperforms existing best practices with 99.75% and 96.20% accuracy on OASIS and AD-Dataset. This paper also performed a domain adaptation test on the OASIS dataset, which obtained 83.25% accuracy.

Crowd behavior prediction (CBP) and crowd counting (CC) are the essential functions of vision-based crowd analysis (CA), which play a crucial role in controlling crowd disasters. The CA using different models for the CBP and the CC will increase computational overheads and have synchronization issues. The state-of-the-art approaches utilized deep convolutional architectures to exploit spatial-temporal features to accomplish the objective, but such models suffer from computational complexities during convolution operations. Thus, to sort out the issues as mentioned earlier, this paper develops a single deep model which performs two functionalities of CA: CBP and CC. The proposed model uses multilayers of depth-wise separable CNN (DSCNN) to extract fine-grained spatial-temporal features from the scene. The DSCNN can minimize the number of matrix multiplications during convolution operation compared to traditional CNN. Further, the existing datasets are available to accomplish the single functionality of CA. In contrast, the proposed model needs a dual-tasking CA dataset which should provide the ground-truth labels for CBP and CC. Thus, a dual functionality CA dataset is prepared using a benchmark crowd behavior dataset, i.e. MED. Around 41 000 frames have been manually annotated to obtain ground-truth crowd count values. This paper also demonstrates an experiment on the proposed multi-functional dataset and outperforms the state-of-the-art methods regarding several performance metrics. In addition, the proposed model processes each test frame at 3.40 milliseconds, and thus is easily applicable in real-time.