Hemin Ali Qadir

Salahaddin University - Erbil | SUH · Department of Electrical Engineering

Background Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning of MRI tumor features could improve postoperative pLGG risk stratification. Methods We used pre-trained deep learning (DL) tool designed for pLGG se...

This paper presents a deep learning (DL) approach for predicting survival probabilities of renal cancer patients based solely on preoperative CT imaging. The proposed approach consists of two networks: a classifier- and a survival- network. The classifier attempts to extract features from 3D CT scans to predict the ISUP grade of Renal cell carcinom...

Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The...

Deep learning (DL) has proven itself as a powerful tool to capture patterns that human eyes may not be able to perceive when looking at high-dimensional data such as radiological data (volumetric data). For example, the classification or grading of kidney tumors in computed tomography (CT) volumes based on distinguishable patterns is a challenging...

An efficient deep learning model that can be implemented in real-time for polyp detection is crucial to reducing polyp miss-rate during screening procedures. Convolutional neural networks (CNNs) are vulnerable to small changes in the input image. A CNN-based model may miss the same polyp appearing in a series of consecutive frames and produce unsub...

The extent to which advanced waveform analysis of non-invasive physiological signals can diagnose levels of hypovolemia remains insufficiently explored. The present study explores the discriminative ability of a deep learning (DL) framework to classify levels of ongoing hypovolemia, simulated via novel dynamic lower body negative pressure (LBNP) mo...

Synthetic polyp generation is a good alternative to overcome the privacy problem of medical data and the lack of various polyp samples. In this study, we propose a deep learning-based polyp image generation framework that generates synthetic polyp images that are similar to real ones. We suggest a framework that converts a given polyp image into a...

Synthetic polyp generation is a good alternative to overcome the privacy problem of medical data and the lack of various polyp samples. In this study, we propose a deep learning-based polyp image generation framework that generates synthetic polyp images that are similar to real ones. We suggest a framework that converts a given polyp image into a...

For polyp detection, we adapt a Faster R-CNN (Ren et al. 2015) architecture shown in Fig. 11.1. Faster R-CNN has two stages: region proposal network (RPN), and a box classifier network. Both stages share a common set of convolutional layers as a feature extractor to reduce the marginal cost for detection. The RPN utilizes feature maps of the last c...

To decrease colon polyp miss-rate during colonoscopy, a real-time detection system with high accuracy is needed. Recently, there have been many efforts to develop models for real-time polyp detection, but work is still required to develop real-time detection algorithms with reliable results. We use single-shot feed-forward fully convolutional neura...

Deep learning has delivered promising results for automatic polyp detection and segmentation. However, deep learning is known for being data-hungry, and its performance is correlated with the amount of available training data. The lack of large labeled polyp training images is one of the major obstacles in performance improvement of automatic polyp...

This paper presents our method for automatic segmentation for kidney and tumor as part of the Kidney Tumor Segmentation Challenge (KiTS19). The KiTS19 Challenge had released a dataset of 300 unique kidney cancer patients, with manual annotations done by Climb 4 Kidney Cancer (C4KC). Here we have proposed our new combined cascade deep learning (DL)...

Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape, and texture...

One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this paper, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp p...

Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this study, we apply a recent region based convolutional neural network (CNN) approach for the automatic detection of polyps in ima...

Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%. However, this computerization is still an unsolved problem due to various polyp-like structures in the colon and high interclass polyp variations in terms of size, color, shape, and texture...

Automatic polyp detection has been shown to be difficult due to various polyp-like structures in the colon and high interclass variations in polyp size, color, shape, and texture. An efficient method should not only have a high correct detection rate (high sensitivity) but also a low false detection rate (high precision and specificity). The state-...

This paper presents our method for automatic segmentationfor kidney and tumor as part of the Kidney Tumor Segmentation Chal-lenge (KiTS19). The KiTS19 Challenge had released a dataset of 300unique kidney cancer patients, with manual annotations done by Climb4 Kidney Cancer (C4KC). Here we have proposed our new combinedcascade deep learning (DL) app...

One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this study, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp p...

Automatic image detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size and color, and the existence of various polyp-like mimics during colonoscopy. In this study, we apply a recent region based convolutional neural network (CNN) approach for the automatic detection of polyps i...

Artificial Intelligence (AI) is a unique innovation. It is different in the way that it has ability to think, reason, and solve problems. The efforts now is towards making general AI which mimics human brain capability. It is expected that AIs will affect almost everything connected to human life e.g. ethics, privacy, security, employment, economy,...

Operating in a degraded visual environment due to darkness can pose a threat to navigation safety. Systems have been developed to navigate in darkness that depend upon differences between objects such as temperature or reflectivity at various wavelengths. However, adding sensors for these systems increases the complexity by adding multiple componen...

We presented a system to display nightime imagery with natural colors using a public database of images. We initially combined two spectral bands of images, thermal and visible, to enhance night vision imagery, however the fused image gave an unnatural color appearance. Therefore, a color transfer based on look-up table (LUT) was used to replace th...

We presented a method for colorizing fused imagery using a synthetic image as the color source. Imagery acquired at night from two sensors with different spectral bands were fused into a single image. We used a color transfer method based on a look-up table approach to change the false color appearance of the fused image to a natural appearance. Be...

We presented a system to display nightime imagery with natural colors using a public database of images. We initially combined two spectral bands of images, thermal and visible, to enhance night vision imagery, however the fused image gave an unnatural color appearance. Therefore, a color transfer based on look-up table (LUT) was used to replace th...