Dr.a.p.j.abdul Kalam Images Download
Echo Wardon
The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.
The brain of a human and other organisms is affected by the electromagnetic field (EMF) radiations, emanating from the cell phones and mobile towers. Prolonged exposure to EMF radiations may cause neurological changes in the brain, which in turn may bring chemical as well as morphological changes in the brain. Conventionally, the identification of EMF radiation effect on the brain is performed using cellular-level analysis. In the present work, an automatic image processing-based approach is used where geometric features extracted from the segmented brain region has been analyzed for identifying the effect of EMF radiation on the morphology of a brain, using drosophila as a specimen. Genetic algorithm-based evolutionary feature selection algorithm has been used to select an optimal set of geometrical features, which, when fed to the machine learning classifiers, result in their optimal performance. The best classification accuracy has been obtained with the neural network with an optimally selected subset of geometrical features. A statistical test has also been performed to prove that the increase in the performance of classifier post-feature selection is statistically significant. This machine learning-based study indicates that there exists discrimination between the microscopic brain images of the EMF-exposed drosophila and non-exposed drosophila. Graphical abstract Proposed Methodology for identification of radiofrequency electromagnetic radiation (RF-EMR) effect on the morphology of brain of Drosophila.
#MissileMan #WorldStudentsDay
"I'm not a Handsome guy but I can give my HAND-TO-SOME one who needs help. Beauty is in heart not in face" -Dr A.P.J. Abdul Kalam
Happy Birthday Missile man #apjabdulkalam @VivekSh07556918 @Rohanvats17 pic.twitter.com/VAgkITzBGb
On the occasion of the birth anniv of the great BharatRatna, Scientist, Dr Avul Pakir Jainulabdeen Abdul Kalam ji. I sincerely pay my condolences to him. From 2015 onwards,UN declared World Students Day on his birth anniv.#MissileManofIndia #apjabdulkalam #WorldStudentsDay pic.twitter.com/V7r251NsD0
There is no other inspiration for youth like #apjabdulkalam sir till date..
Remembering The 11th President of India, Missile Man, Bharat Ratna Dr APJ Abdul Kalam on his 89th Birth Anniversary.#bharatRatna pic.twitter.com/mHFvVsC7s0
Researchers at the NASA's Jet Propulsion Laboratory (JPL) had discovered a new bacterium on the filters of the International Space Station (ISS) and named it Solibacillus kalamii to honour the late president Dr. A. P. J. Abdul Kalam.[161]
An image processing-based method to count RBCs is proposed by Acharya & Kumar (2018), where normal and abnormal cells were identified for an input blood smear image. For this purpose, the K-medoids algorithm is used to extract WBCs from a blood smear image, and granulometric-based analysis is performed to separate WBCs and RBCs. Circular Hough Transform and labeling are used to count the number of cells. Another image processing-based RBC counting method is presented where thresholding is done for different pixels of HSV converted images (Cruz et al., 2017). Counting blood cells is done using the connected component labeling. Circular Hough transform (Acharjee et al., 2016), CHT (Sarrafzadeh et al., 2015), SVM, and spectral angle imagining (Lou et al., 2016) are also used to count different blood cells. ResNet and Inception net-based pre-trained models were used to count WBC from the image after segmentation using color space analysis (Habibzadeh et al., 2018). An iterative circle detection algorithm is also used to find RBCs and WBCs (Alomari et al., 2014). Another method has been presented for segmenting and counting RBCs using pulse-coupled neural networks (Ma et al., 2016). Counting cells is based on the average size of RBC, which can create mismatching in some cases. The blood cell counting using different deep learning models is compared on BCCD datasets which achieved the highest mean average precision (mAP) of 74.37% (Alam & Islam, 2019).
The main contribution of this paper is an automatic blood cell detection and counting framework. The dataset of 364 images having 4888 blood cells is labeled and divided into training, validation, and test set. The whole dataset is used to train convolutional neural networks in different batch sizes. The performance of the trained model is analyzed in different parameters. Bounding boxes were made around the detected blood cells. Mean average precision is high. The average precision of WBCs is more than 97% for all models. The counting error is much less for detected blood cells with a detection threshold of 0.9, and WBCs cells give 100% accurate results on counting. The detection time for processing one blood smear image of size 640480 pixels takes
In this study, the blood cell images database was collected from an opensource repository; it is the Blood Cell Count Dataset (BCCD), which has 364 images of blood cells. These images contain 4888 different cells: 4155 RBCs cells, 372 WBCs cells, and 361 platelets (BCCD, 2020). Figure 1 shows RBCs, WBCs, and platelets on an input blood smear image. The deep learning model is trained with 256 images, and the model is validated with 54 blood cell images. Once the model is generated, it is tested with 54 blood cell images having 800 different blood cells, and counting is performed.
The input blood cell images are divided into N X N grids. Grid cells are responsible for detecting objects if the centers of the objects lie in those grid cells: they predict bounding boxes and determine the confidence score associated with those boxes. YOLO-v3 calculates bounding boxes on three different scales like features pyramid network (Lin et al., 2017), and these prediction results are more significant for detecting minor-sized blood cell targets. The algorithm was already trained on COCO datasets (Lin et al., 2014) and implemented using pre-trained weights.
The input blood cell images are divided into N X N grids. Grid cells are responsible for detecting objects if the centers of the objects lie in those grid cells: they predict bounding boxes and determine the confidence score associated with those boxes. YOLO-v3 calculates bounding boxes on three different scales like features pyramid network (Lin et al., 2017), and these prediction results are more significant for detecting minor-sized blood cell targets. The algorithm was already trained on COCO datasets (Lin et al., 2014) and implemented using pre-trained weights. Predictions were made on three different scales in the proposed work, as shown in Fig. 3. Thus, an input smear image of 416 x 416 dimension was divided into grids of 13 13, 26 26, and 52 52 for the respective stride values of 32, 16, and 8. The confidence score describes the confidence of the model that the object lies in the box and the accuracy of the box it predicted. Each grid in the input image predicts B bounding boxes with confidence scores and C class conditional probabilities. The confidence score formula is given in equation 1:
A convolutional neural network needs to be trained and tested to develop a framework for blood cell detection. For this purpose, an Intel Xenon processor with 64 GB RAM and an NVIDIA Quadro P600 graphics processing unit (GPU) with 24 GB graphics memory are used. Image pre-processing, training, and testing are done on Anaconda3 (Python 3.7), and other libraries, such as TensorFlow and OpenCV, required to train the model. The statistics of the number of images in the datasets are given in Table 1.
A dataset from all sources is divided for training, testing, and validation in 70%, 15%, and 15%, respectively. The deep learning model is trained with blood cell images containing 4888 cells for several iterations until the loss becomes saturated. Generated trained models are analyzed with multiple images in test datasets to obtain overall performance. Yolo-v3 based convolutional neural network is trained for 400 epochs with an initial learning rate of 10-3 and IoU of 50%.
The training is done for different batch sizes of 4, 8, and 16 images. Its steps in each epoch are 512, 256, and 128 while training in a batch size of 4, 8, and 16, respectively. Once trained, models are tested with never-seen blood cell smear images. Some of the tested results are shown in Figure 4, where various blood cells are detected with a higher percentage using the proposed framework.
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