An improved diagnostic algorithm based on deep learning for ischemic stroke detection in posterior fossa

Ischemic stroke is triggered by an obstruction in the blood vessel of the brain, preventing the blood to flow to the brain tissues region. Solving this is extremely beneficial as Non-enhanced Computed Tomography (NECT) has significant shortcomings for posterior fossa (PF): (i) deficient sensitivity (ii) subtle finding and (iii) radiation exposure. Consequently, PF ischemic stroke lesions are missed at the early stage which increasing the mortality rates. Nowadays, the development of Computer-Aided Diagnosis (CAD) is increasingly becoming an important area in stroke detection. Despite the rapid development of CAD in stroke diagnosis, no studies have been found on stroke detection in PF. Until today, manual delineation of ischemic stroke in PF on NECT demands dealing with a large amount of data, which leads to late prognosis. As the amount of image data generated by NECT is massive, Deep Learning (DL) solutions are among the effective ways to deal with complex and large amount of cross-sectional data. Therefore, a new diagnostic algorithm based on DL is proposed for ischemic stroke detection in PF. The algorithm framework consists of hybrid of improved Xception model and YOLO V2 detector to classify the PF slices with ischemic and localise the infarction in classified slices, respectively. Following that, a CAD system is established by integrating the proposed algorithmic framework. The performance and effectiveness of the proposed algorithmic are evaluated by the comparison with the gold standard provided by the radiologists. The proposed algorithmic framework has shown to be less prone to overfitting and simultaneously improves the detection performance than the original DL model. The results demonstrate that the performance measure of 90.77% has been recorded for detection rate with average processing time of 1.02 to 1.04 seconds per image. The developed algorithm is reported to be reliable to assist the radiologist in ischemic PF diagnosis which is important for future healthcare needs

Deep transfer learning application for automated ischemic classification in posterior fossa CT images

Abstract—Computed Tomography (CT) imaging is one of the conventional tools used to diagnose ischemic in Posterior Fossa (PF). Radiologist commonly diagnoses ischemic in PF through CT imaging manually. However, such a procedure could be strenuous and time consuming for large scale images, depending on the expertise and ischemic visibility. With the rapid development of computer technology, automatic image classification based on Machine Learning (ML) is widely been developed as a second opinion to the ischemic diagnosis. The practical performance of ML is challenged by the emergence of deep learning applications in healthcare. In this study, we evaluate the performance of deep transfer learning models of Convolutional Neural Network (CNN); VGG-16, GoogleNet and ResNet-50 to classify the normal and abnormal (ischemic) brain CT images of PF. This is the first study that intensively studies the application of deep transfer learning for automated ischemic classification in the posterior part of brain CT images. The experimental results show that ResNet-50 is capable to achieve the highest accuracy performance in comparison to other proposed models. Overall, this automatic classification provides a convenient and time-saving tool for improving medical diagnosis

Vision Based Human Decoy System for Spot Cooling

This project aims to reduce the energy consumption of air conditioner usage while maintaining occupant comfort. Cooling down the unoccupied space can be considered as waste of energy. Therefore, a human decoy system is proposed to track any human in the detection area. Image contains depth data in each pixel which can be used to detect the presence of target subject as well as their position. The acquired position data is processed by using MATLAB and subsequently is transmitted to Arduino Mega using serial communication to control stepper motors. The experimental results show that the air conditioner airflow is successfully can be directed to the target human subject with average response of 0.860 seconds per movement within detection area

Comparative performance of filtering methods for reducing noise in ischemic posterior fossa CT Images

deepest part of the brain. This appears to cause significant degradation in CT image quality due to the effect of beam hardening and bone thickness. Whilst these issues may not fundamentally limit the scanning procedure, it does appear to be the contributory factors in reducing the performance of the ischemic diagnosis procedure. Thus, it is seen that image filtering is playing an important role in improving the CT image quality and effectively eliminates the influence of Poisson noise in the PF region. Therefore, this paper attempts to assess the feasibility of four different filtering methods; Anisotropic diffusion, Bilateral, Median and Wiener to eliminate noise in the CT image of PF containing ischemic. To the best of our knowledge, this is the first study to report the performance of filtering in ischemic PF. The efficacy of these four filters is evaluated in details using qualitative and quantitative metrics such as Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE), Structural Similarity Index (SSIM) and processing time. The experimental works demonstrate that Bilateral filtering offers promising results in which this method can eliminate Poisson noise in CT images for ischemic PF with average PSNR, RMSE, SSIM values of 32.95, 5.7416 and 0.9749 respectively. This filter has provided the flexibility of being applicable even in cases where ischemic is presented in PF

Wearable flex sensor system for multiple badminton player grip identification

This paper focuses on the development of a wearable sensor system to identify the different types of badminton grip that is used by a player during training. Badminton movements and strokes are fast and dynamic, where most of the involved movement are difficult to identify with the naked eye. Also, the usage of high processing optometric motion capture system is expensive and causes computational burden. Therefore, this paper suggests the development of a sensorized glove using flex sensor to measure a badminton player’s finger flexion angle. The proposed Hand Monitoring Module (HMM) is connected to a personal computer through Bluetooth to enable wireless data transmission. The usability and feasibility of the HMM to identify different grip types were examined through a series of experiments, where the system exhibited 70% detection ability for the five different grip type. The outcome plays a major role in training players to use the proper grips for a badminton stroke to achieve a more powerful and accurate stroke execution