This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context medical imaging. Medical imaging plays critical role diagnosis treatment diseases, CNN-based models have demonstrated significant improvements image analysis classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise addressing challenges related to small datasets limited computational resources. reviews advantages imaging,...

Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most models found literature segment images with respect different modalities. Therefore, goal of paper examine numerous advancements innovations U-Net architecture, as well recent trends, aim highlighting ongoing potential being better...

Alzheimer 's Disease (AD) is the most common form of dementia that can lead to a neurological brain disorder causes progressive memory loss as result damaging cells and ability perform daily activities. Using MRI (Magnetic Resonance Imaging) scan images, we get help Artificial intelligence (AI) technology for detection prediction this disease classify AD patients whether they have or may not deadly in future. The main purpose doing all make best tools radiologists, doctors, caregivers save...

We present a deep learning approach towards the large-scale prediction and analysis of bird acoustics from 100 different species. use spectrograms constructed on audio recordings Cornell Bird Challenge (CBC)2020 dataset, which includes multiple potentially overlapping vocalizations with background noise. Our experiments show that hybrid modeling involves Convolutional Neural Network (CNN) for representation slice spectrogram, Recurrent (RNN) temporal component to combine across time-points...

The health system in today's real world is significant but difficult and overcrowded. These hurdles can be diminished using improved record management blockchain technology. technologies handle medical data to provide security by monitoring maintaining patient records. processing of records essential analyze the earlier prescribed medicines understand severity diseases. Blockchain technology improve security, performance, transparency sharing current healthcare system. This paper proposed a...

The aedes mosquito-borne dengue viruses cause fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, maximum number cases ever testified worldwide, prompting WHO label virus one world's top ten public health risks. Dengue hemorrhagic fever can progress into shock syndrome, which be fatal. also advance syndrome. To provide accessible and timely supportive care therapy, it is necessary have...

Because it is associated with most multifactorial inherited diseases like heart disease, hypertension, diabetes, and other serious medical conditions, obesity a major global health concern. Obesity caused by hereditary, physiological, environmental factors, as well poor nutrition lack of exercise. Weight loss can be difficult for various reasons, diagnosed via BMI, which used to estimate body fat people. Muscular athletes, example, may have BMI in the range even when they are not obese....

Brain tumor segmentation from Magnetic Resonance Images (MRI) is considered a big challenge due to the complexity of brain tissues, and segmenting these tissues healthy an even more tedious when manual undertaken by radiologists. In this paper, we have presented experimental approach emphasize impact effectiveness deep learning elements like optimizers loss functions towards optimal solution for segmentation. We evaluated our performance results on most popular datasets (MICCAI BraTS 2020...

Karyotying is the process of pairing and ordering 23 pairs human chromosomes from cell images on basis size, centromere position, banding pattern. Karyotyping during metaphase often used by clinical cytogeneticists to analyze for diagnostic purposes. It requires experience, domain expertise considerable manual effort efficiently perform karyotyping diagnosis various disorders. Therefore, automation or even partial highly desirable assist technicians reduce cognitive load necessary...

The solution of a partial differential equation can be obtained by computing the inverse operator map between input and space. Towards this end, we introduce \textit{multiwavelet-based neural learning scheme} that compresses associated operator's kernel using fine-grained wavelets. By explicitly embedding multiwavelet filters, learn projection onto fixed polynomial bases. projected is trained at multiple scales derived from repeated computation transform. This allows complex dependencies...

Abstract Chronic obstructive pulmonary disease (COPD) is one of the leading causes death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because test depends on an adequate effort from tester and testee. Moreover, early challenging. The authors address detection by constructing two novel physiological signals datasets (4432 records 54 patients in WestRo dataset 13824 medical 534 Porti dataset). demonstrate their complex coupled fractal dynamical characteristics...

A face image not only provides details about the identity of a subject but also reveals several attributes such as gender, race, sexual orientation, and age. Advancements in machine learning algorithms popularity sharing images on World Wide Web, including social media websites, have increased scope data analytics information profiling from photo collections. This poses serious privacy threat for individuals who do want to be profiled. research presents novel algorithm anonymizing selective...

This paper focuses on analysis and design of time-varying complex networks having fractional order dynamics. These systems are key in modeling the dynamical processes arising several natural man made systems. Notably, examples include neurophysiological signals such as electroencephalogram (EEG) that captures variation potential fields, blood oxygenation level dependent (BOLD) signal, which serves a proxy for neuronal activity. Notwithstanding, originated by locally measuring EEG BOLD often...

Deep learning is one of the machine approach which has shown promising results and performance as compare to traditional algorithms in terms high dimensional data MRI brain image. In this article application deep medical field addressed. A thorough review various for diagnosis Alzheimer’s disease done, a progressive disorder that destroy memory gradually, it common older adults caused by dementia. It been obtained most research papers widely used represented algorithm Convolutional Neural...

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting possibility AD, contrast cognitive testing brain structure analyses. We used an MRI dataset that downloaded from Kaggle source train our network. Utilizing temporal memory characteristics LSTMs, network was created efficiently...

<title>Abstract</title> Fruit is an important part of daily diet around the globe. Automatic fruit classification and recognition ill-posed problem. Till now many machines learning models have been developed to fruits. However, performance these techniques reduced during poor weather environmental conditions in real-time processing applications. It quite challenging automatically classify fruits from images, when images are captured a different viewing angle. This paper proposes in-field...