Manifestations of brain tumors can trigger various psychiatric symptoms. Brain tumor detection can efficiently solve or reduce chances of occurrences of diseases, such as Alzheimer's disease, dementia-based disorders, multiple sclerosis and bipolar disorder. In this paper, we propose a segmentation-based approach to detect brain tumors in MRI11. We provide a comparative study between two different U-Net architectures (U-Net: baseline and U-Net: ResNeXt50 backbone) and a Feature Pyramid Network (FPN) that are trained/validated on the TCGA-LGG dataset of size 3, 929 images. U-Net architecture with ResNeXt50 backbone achieves the best Dice coefficient of 0.932, while baseline U-Net and FPN separately achieve Dice coefficients of 0.846 and 0.899, respectively. The results obtained from U-Net with ResNeXt50 backbone outperform previous works.

Gastric malignancy - one of the five most deadliest types of cancer - exceeds annual cases by a million worldwide since 2017. Automated screening tools may help speed up the screening and clinical procedures. In this paper, we propose a binary classification approach to classify gastrointestinal cancer tissues, namely Microsatellite Instable (MSI) and Microsatellite Stable (MSS) through stacked generalization based ensemble Deep Neural Network (DNN). Using a dataset of size 192, 315 images, we achieve an overall accuracy of 94.91% and sensitivity of 95.95%. Our results outperform previous works.

With over 200K cases in the U.S. alone, retinal disorders are the most common cause of irreversible blindness. This serves as a primary aim to analyze automated screening tools to detect retinal disorders. We analyze the OCT dataset (84, 484 images) and enhance the images by using Generative Adversarial Networks (GANs). This work specifically focuses on enhancing the quality of source (training) images for better algorithm validatiorr/testing11Authors contributed equally to the work.. We synthesize super resolution-based images using generators, discriminators and the adversarial nature of the GANs. The performance of the Ret-GAN is validated by PSNR, SSIM, and loss functions. To test the Ret-GAN generated images, we train a convolutional neural network (CNN) with the original dataset images and super-resolution images. We achieve an accuracy of 0.9825 on Ret-GAN generated image data, and 0.9525 on the original data. We statistically analyze the CNN with a number of evaluation metrics to further validate the results. The proposed scheme is compared to benchmark research findings on the same dataset. Our results are encouraging.

In this paper, we deconstruct and demonstrate a detection framework to classify Retinal Optical Coherence Tomography (OCT) images across three classes namely, Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), and the DRUSEN from normal Retina. In this research, we developed on a Deep Ensemble Network by the virtue of which we were able to obtain a state-of-the-art accuracy of 98.53% on our test image dataset that was deliberately increased to 12% of the total images. Further, we also took advantage and insight from a feature map obtained from our convolutional layers to build our final model, which we call Optical Coherence Tomography Extended (OCTx). In our experiments, we found that OCTx was more accurate and diverse as compared to previously reported works that were validated on the exact same dataset.