CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection.

Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which utilizes deep learning methods to improve the detection of cataract states. The aim is to create a framework that is more lightweight and adaptable for use in environments or devices with limited memory or storage capacity. This involves reducing the number of trainable parameters while still allowing for effective learning of representations from data. Additionally, the framework is designed to be suitable for real-time or near-real-time applications where rapid inference is essential. This study utilizes cataract and normal images from the Ocular Disease Intelligent Recognition (ODIR) database. The suggested model employs smaller kernels, fewer training parameters, and layers to efficiently decrease the number of trainable parameters, thereby lowering computational costs and average running time compared to other pre-trained models such as VGG19, ResNet50, DenseNet201, MIRNet, Inception V3, Xception, and Efficient net B0. The experimental results illustrate that the proposed approach achieves a binary classification accuracy of 97.24% (normal or cataract) and an average cataract state detection accuracy of 98.17% (normal, grade 1-minimal cloudiness, grade 2-immature cataract, grade 3-mature cataract, and grade 4-hyper mature cataract), competing with state-of-the-art cataract detection methods. The resulting model is lightweight at 17 MB and has fewer trainable parameters (175, 617), making it suitable for deployment in environments or devices with constrained memory or storage capacity. With a runtime of 212 ms, it is well-suited for real-time or near-real-time applications requiring rapid inference.

Identification of Optimal and Most Significant Event Related Brain Functional Network.

Advancements in network science have facilitated the study of brain communication networks. Existing techniques for identifying event-related brain functional networks (BFNs) often result in fully connected networks. However, determining the optimal and most significant network representation for event-related BFNs is crucial for understanding complex brain networks. The presence of both false and genuine connections in the fully connected network requires network thresholding to eliminate false connections. However, a generalized framework for thresholding in network neuroscience is currently lacking. To address this, we propose four novel methods that leverage network properties, energy, and efficiency to select a generalized threshold level. This threshold serves as the basis for identifying the optimal and most significant event-related BFN. We validate our methods on an openly available emotion dataset and demonstrate their effectiveness in identifying multiple events. Our proposed approach can serve as a versatile thresholding technique to represent the fully connected network as an event-related BFN.

Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD.

Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging data-sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer's disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network-based applications.