Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by blankets.Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns.Consequently,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and comfort.This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone right.The study involves 10 participants under two conditions:with and without blankets.Initially,the database is normalized into a video frame.The subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 model.In the third phase,the extracted features are utilized for classification.The fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal datasets.Finally,the fused vectors are passed to machine learning classifiers for final classification.The dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic Support Vector Machine(QSVM)without the blanket.Moreover,the Linear SVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.

Robust Deep Learning Model for Black Fungus Detection Based on Gabor Filter and Transfer Learning

Black fungus is a rare and dangerous mycology that usually affects the brain and lungs and could be life-threatening in diabetic cases.Recently,some COVID-19 survivors,especially those with co-morbid diseases,have been susceptible to black fungus.Therefore,recovered COVID-19 patients should seek medical support when they notice mucormycosis symptoms.This paper proposes a novel ensemble deep-learning model that includes three pre-trained models:reset(50),VGG(19),and Inception.Our approach is medically intuitive and efficient compared to the traditional deep learning models.An image dataset was aggregated from various resources and divided into two classes:a black fungus class and a skin infection class.To the best of our knowledge,our study is the first that is concerned with building black fungus detection models based on deep learning algorithms.The proposed approach can significantly improve the performance of the classification task and increase the generalization ability of such a binary classification task.According to the reported results,it has empirically achieved a sensitivity value of 0.9907,a specificity value of 0.9938,a precision value of 0.9938,and a negative predictive value of 0.9907.

An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification

Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of diseases.The brain is an essential organ in humans.Brain tumors cause loss of memory,vision,and name.In 2020,approximately 18,020 deaths occurred due to brain tumors.These cases can be minimized if a brain tumor is diagnosed at a very early stage.Computer vision researchers have introduced several techniques for brain tumor detection and classification.However,owing to many factors,this is still a challenging task.These challenges relate to the tumor size,the shape of a tumor,location of the tumor,selection of important features,among others.In this study,we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning features.In the proposed framework,initially,a database is normalized in the form of high-grade glioma(HGG)and low-grade glioma(LGG)patients and then two pre-trained deep learning models(ResNet50 and Densenet201)are chosen.The deep learning models were modified and trained using transfer learning.Subsequently,the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep models.The selected features are fused using a serial-based approach and classified using a cubic support vector machine.The experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8%and 84.6%for HGG and LGG,respectively.The comparison is performed using several classification methods,and it shows the significance of our proposed technique.