COVID-19 is an infectious viral disease that mostly affects the lungs. That quickly spreads across the world. Early detection of the virus boosts the chances of patients recovering quickly worldwide. Many radiographic techniques are used to diagnose an infected person such as X-rays, deep learning technology based on a large amount of chest x-ray images is used to diagnose COVID-19 disease. Because of the scarcity of available COVID-19 X-rays image, the limited COVID-19 Datasets are insufficient for efficient deep learning detection models. Another problem with a limited dataset is that training models suffer from over-fitting, and the predictions are not generalizable to address these problems. In this paper, we developed Conditional Generative Adversarial Networks (CGAN) to produce synthetic images close to real images for the COVID-19 case and traditional augmentation that was used to expand the limited dataset then used to train by Customized deep detection model. The Customized Deep learning model was able to obtain excellent detection accuracy of 97% accurate with only ten epochs. The proposed augmentation outperforms other augmentation techniques. The augmented dataset includes 6988 high-quality and resolution COVID-19 X-rays images. At the same time, the original COVID-19 X-rays images are only 587.

Deep Learning (DL) models have outperformed remarkably and effectively on several Computers Vision applications. However, these models require large amounts of data to avoid overfitting problems. Overfitting happens when a network trains a function with an incredibly high variance to represent the training data perfectly. Consequently, medical images lack to availability of large labeled datasets, and the annotation of medical images is expensive and time-consuming for experts, as the COVID-19 virus is an infectious disease, these datasets are scarce and it is difficult to get large datasets. The limited amount of the COVID-19 class compared to any other classes, for example (healthy). To solve the scarcity data problem, we adjust a Conditional Generative Adversarial Network (CGAN) as a solution to the problems of scarcity and limited data. CGAN contains two neural networks: a generator that creates synthetic (fake) images, and a discriminator that recognizes a real sample of training and a generated sample from the generator. The adjusted CGAN is able to Generate synthetic images with high resolution and close to the original images which aid in expanding the limited dataset specific to a new pandemic. In addition to CGAN augmenting strategies, this research also briefly explores additional aspects of data augmentation like time augmentation and total dataset size. Frechet inception distance metric (FID) has been used for evaluating synthetic images generated by CGAN. The adjusted CGAN obtains better FID results for the high-resolution synthetic X-rays images it achieves 2.349%.

The complexity of segmenting a brain tumour is critical in medical image processing. Treatment options and patient survival rates can only be improved if brain tumours can be prevented and treated. Segmentation of the brain is the most complex and time-consuming task to diagnose cancer utilizing a manual approach for numerous magnetic resonance images (MRI). The aim of MRI brain tumour image segmentation that to build an automated magnetic resonance imaging tumour segmentation system with separate the area of tumour and provided a clear boundary of the tumour region. U-Nets with different transfer learning models as backbones are presented in this paper, there are ResNet50, DenseNet169 and EfficientNet-B7. Brain lesion segmentation is performed using the multimodal brain tumor segmentation challenge 2020 dataset (BraTS2020). Based on MRI scans of the brain, the tumor segmentation technique is assessed using F1-score, Dice loss, and intersection over union score (IoU). The U-Net encoder used with EfficientNet-B7 outperforms all other architectures in terms of performance metrics across the board. Overall, the results of this experiment are rather excellent. The Dice-loss score was 0.009435, and the score of IoU was 0.7435, F1-score was 0.9848, accuracy was 0.9924, precision was 0.9829, recall was 0.9868, and specificity was 0.9943. The U-Net with EfficientNet-B7 architecture was shown to be crucial in the treatment of brain tumors, according to the findings of the experiments

A global pandemic has emerged as a result of the widespread coronavirus disease (COVID-19). Deep learning (DL) techniques are used to diagnose COVID-19 based on many chest X-ray. Due to the scarcity of available X-ray images, the performance of DL for COVID-19 detection is lagging, underdeveloped, and suffering from overfitting. Overfitting happens when a network trains a function with an  incredibly high variance to represent the training data perfectly. Consequently, medical images lack the availability of large labeled datasets, and the annotation of medical images is expensive and time-consuming for experts. As the COVID-19 virus is an infectious disease, these datasets are scarce, and it is difficult to get large datasets due to patient privacy. To address these issues by augmenting the COVID-19 dataset. In this paper, we adjusted conditional generation adversarial networks (CGAN) along with traditional augmentation (TA). The augmented dataset includes 6550 X-ray images that can be used to improve the diagnosis of COVID-19, and we have implemented five models of transfer learning procedures (DTL). The proposed procedures yielded high detection accuracy of 95%, 93%, 92%, and 92% in only ten epochs, for VGG-16, VGG-19, Xception, and Inception, respectively, and a custom convolutional neural network. Experimental results prove that our model achieves a high detection accuracy of up to 96% compared to other models. We hope it can be applied in other fields with rare data sets.

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