Noor Ayesha

TL;DR: A new deep learning‐based method is proposed for microscopic brain tumor detection and tumor type classification and a comparison with existing techniques shows the proposed design yields comparable accuracy.

TL;DR: In this article, the authors presented segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU).

TL;DR: Wang et al. as mentioned in this paper presented deep learning-based COVID-19 detection using CT and X-ray images and data analytics on its spread worldwide, and their research structure built on a recent analysis of the COVID19 data and prospective research to systematize current resources, help the researchers, practitioners by using in-depth learning methodologies to build solutions.

TL;DR: In this paper, a novel lung cancer detection technique has been developed using machine learning techniques, which comprises feature extraction, fusion using patch base LBP (Local Binary Pattern) and discrete cosine transform (DCT).

TL;DR: In this article, a two-stage classifier was used to predict diabetic retinopathy (DR), a disease of the human eye that causes retinal damage in diabetic patients and ultimately leads to complete blindness.