Pavan Kumar S

Pandiyaraju V, Shravan Venkatraman, Abeshek A et al.

Farmers face various challenges when it comes to identifying diseases in rice leaves during their early stages of growth, which is a major reason for poor produce. Therefore, early and accurate disease identification is important in agriculture to avoid crop loss and improve cultivation. In this research, we propose a novel hybrid deep learning (DL) classifier designed by extending the Squeeze-and-Excitation network architecture with a channel attention mechanism and the Swish ReLU activation function. The channel attention mechanism in our proposed model identifies the most important feature channels required for classification during feature extraction and selection. The dying ReLU problem is mitigated by utilizing the Swish ReLU activation function, and the Squeeze-andExcitation blocks improve information propagation and cross-channel interaction. Upon evaluation, our model achieved a high F1-score of 99.76% and an accuracy of 99.74%, surpassing the performance of existing models. These outcomes demonstrate the potential of state-of-the-art DL techniques in agriculture, contributing to the advancement of more efficient and reliable disease detection systems.

Shravan Venkatraman, Pandiyaraju V, Abeshek A et al.

Being the most commonly known neurodegeneration, Alzheimer's Disease (AD) is annually diagnosed in millions of patients. The present medical scenario still finds the exact diagnosis and classification of AD through neuroimaging data as a challenging task. Traditional CNNs can extract a good amount of low-level information in an image while failing to extract high-level minuscule particles, which is a significant challenge in detecting AD from MRI scans. To overcome this, we propose a novel Granular Feature Integration method to combine information extraction at different scales along with an efficient information flow, enabling the model to capture both broad and fine-grained features simultaneously. We also propose a Bi-Focal Perspective mechanism to highlight the subtle neurofibrillary tangles and amyloid plaques in the MRI scans, ensuring that critical pathological markers are accurately identified. Our model achieved an F1-Score of 99.31%, precision of 99.24%, and recall of 99.51%. These scores prove that our model is significantly better than the state-of-the-art (SOTA) CNNs in existence.

Shravan Venkatraman, Pandiyaraju V, Abeshek A et al.

Brain tumors result from abnormal cell growth in brain tissue. If undiagnosed, they cause neurological deficits, including cognitive impairment, motor dysfunction, and sensory loss. As tumors grow, intracranial pressure increases, potentially leading to fatal complications such as brain herniation. Early diagnosis and treatment are crucial to controlling these effects and slowing tumor progression. Deep learning (DL) and artificial intelligence (AI) are increasingly used to assist doctors in early diagnosis through magnetic resonance imaging (MRI) scans. Our research proposes targeted neural architectures within multi-objective frameworks that can localize, segment, and classify the grade of these gliomas from multimodal MRI images to solve this critical issue. Our localization framework utilizes a targeted architecture that enhances the LinkNet framework with an encoder inspired by VGG19 for better multimodal feature extraction from the tumor along with spatial and graph attention mechanisms that sharpen feature focus and inter-feature relationships. For the segmentation objective, we deployed a specialized framework using the SeResNet101 CNN model as the encoder backbone integrated into the LinkNet architecture, achieving an IoU Score of 96%. The classification objective is addressed through a distinct framework implemented by combining the SeResNet152 feature extractor with Adaptive Boosting classifier, reaching an accuracy of 98.53%. Our multi-objective approach with targeted neural architectures demonstrated promising results for complete glioma characterization, with the potential to advance medical AI by enabling early diagnosis and providing more accurate treatment options for patients.

Pandiyaraju V, Shravan Venkatraman, Abeshek A et al.

Brain tumors are abnormalities that can severely impact a patient's health, leading to life-threatening conditions such as cancer. These can result in various debilitating effects, including neurological issues, cognitive impairment, motor and sensory deficits, as well as emotional and behavioral changes. These symptoms significantly affect a patient's quality of life, making early diagnosis and timely treatment essential to prevent further deterioration. However, accurately segmenting the tumor region from medical images, particularly MRI scans, is a challenging and time-consuming task that requires the expertise of radiologists. Manual segmentation can also be prone to human errors. To address these challenges, this research leverages the synergy of SeNet and ResNet architectures within an encoder-decoder framework, designed specifically for glioma detection and segmentation. The proposed model incorporates the power of SeResNet-152 as the backbone, integrated into a robust encoder-decoder structure to enhance feature extraction and improve segmentation accuracy. This novel approach significantly reduces the dependency on manual tasks and improves the precision of tumor identification. Evaluation of the model demonstrates strong performance, achieving 87% in Dice Coefficient, 89.12% in accuracy, 88% in IoU score, and 82% in mean IoU score, showcasing its effectiveness in tackling the complex problem of brain tumor segmentation.

Pandiyaraju V, Shravan Venkatraman, Pavan Kumar S et al.

Breast cancer is one of the leading causes of death globally, and thus there is an urgent need for early and accurate diagnostic techniques. Although ultrasound imaging is a widely used technique for breast cancer screening, it faces challenges such as poor boundary delineation caused by variations in tumor morphology and reduced diagnostic accuracy due to inconsistent image quality. To address these challenges, we propose novel Deep Learning (DL) frameworks for breast lesion segmentation and classification. We introduce a precision mapping mechanism (PMM) for a precision mapping and attention-driven LinkNet (PMAD-LinkNet) segmentation framework that dynamically adapts spatial mappings through morphological variation analysis, enabling precise pixel-level refinement of tumor boundaries. Subsequently, we introduce a component-specific feature enhancement module (CSFEM) for a component-specific feature-enhanced classifier (CSFEC-Net). Through a multi-level attention approach, the CSFEM magnifies distinguishing features of benign, malignant, and normal tissues. The proposed frameworks are evaluated against existing literature and a diverse set of state-of-the-art Convolutional Neural Network (CNN) architectures. The obtained results show that our segmentation model achieves an accuracy of 98.1%, an IoU of 96.9%, and a Dice Coefficient of 97.2%. For the classification model, an accuracy of 99.2% is achieved with F1-score, precision, and recall values of 99.1%, 99.3%, and 99.1%, respectively.

Shravan Venkatraman, Pavan Kumar S, Rakesh Raj Madavan et al.

Accurate classification of computed tomography (CT) images is essential for diagnosis and treatment planning, but existing methods often struggle with the subtle and spatially diverse nature of pathological features. Current approaches typically process images uniformly, limiting their ability to detect localized abnormalities that require focused analysis. We introduce UGPL, an uncertainty-guided progressive learning framework that performs a global-to-local analysis by first identifying regions of diagnostic ambiguity and then conducting detailed examination of these critical areas. Our approach employs evidential deep learning to quantify predictive uncertainty, guiding the extraction of informative patches through a non-maximum suppression mechanism that maintains spatial diversity. This progressive refinement strategy, combined with an adaptive fusion mechanism, enables UGPL to integrate both contextual information and fine-grained details. Experiments across three CT datasets demonstrate that UGPL consistently outperforms state-of-the-art methods, achieving improvements of 3.29%, 2.46%, and 8.08% in accuracy for kidney abnormality, lung cancer, and COVID-19 detection, respectively. Our analysis shows that the uncertainty-guided component provides substantial benefits, with performance dramatically increasing when the full progressive learning pipeline is implemented. Our code is available at: https://github.com/shravan-18/UGPL