Muhammad Shahzad

• Doctor of Philosophy
• PhD Scholar at Hazara University

Visual analysis of peripheral blood smear slides using medical image analysis is required to diagnose red blood cell (RBC) morphological defor- mities caused by anemia. The absence of a complete anaemic RBC dataset has hindered the training and testing of deep convolutional neural networks (CNNs) for computer-aided analysis of RBC morphology. We in...

Visual analysis of peripheral blood smear slides using medical image analysis is required to diagnose red blood cell (RBC) morphological deformities caused by anemia. The absence of a complete anaemic RBC dataset has hindered the training and testing of deep convolutional neural networks (CNNs) for computer-aided analysis of RBC morphology. We intr...

Blood smear analysis is often used to diagnose diseases like malaria, Anemia, Leukemia, etc. Morphological changes, such as size, shapes, and color, are receiving much attention in pathological analysis. Existing methods for detecting, diagnosing and analyzing blood smears cannot quantify overlapped, irregular boundaries and complex structures. Thi...

Precise segmentation of the nucleus is vital for computer-aided diagnosis (CAD) in cervical cytology. Automated delineation of the cervical nucleus has notorious challenges due to clumped cells, color variation, noise, and fuzzy boundaries. Due to its standout performance in medical image analysis, deep learning has gained attention from other tech...

Rapid development in sketch-to-image translation methods boosts the investigation procedure in law enforcement agencies. But, the large modality gap between manually generated sketches makes this task challenging. Generative adversarial network (GAN) and encoder-decoder approach are usually incorporated to accomplish sketch-to-image generation with...

Despite the idiom not to prejudge something by its outward appearance, we consider deep learning to learn whether we can judge a book by its cover or, more precisely, by its text and design. The classification was accomplished using three strategies, i.e., text only, image only, and both text and image. State-of-the-art CNNs (convolutional neural n...

The healthcare sector is the highest priority sector, and people demand the highest services and care. The fast rise of deep learning, particularly in clinical decision support tools, has provided exciting solutions primarily in medical imaging. In the past, ANNs (artificial neural networks) have been used extensively in dermatology and have shown...

The automatic detection of blood cell elements for identifying morphological deformities is still a challenging research domain. It has a pivotal role in cognition and detecting the severity level of disease. Using a simple microscope, manual disease detection, and morphological disorders in blood cells is mostly time-consuming and erroneous. Due t...

Pixel-level analysis of blood images plays a pivotal role in diagnosing blood-related diseases, especially Anaemia. These analyses mainly rely on an accurate diagnosis of morphological deformities like shape, size, and precise pixel counting. In traditional segmentation approaches, instance or object-based approaches have been adopted that are not...

Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framewor...

Hepatitis C virus (HCV) is a blood born, positive, single stranded RNA strand and circular in shape. The hepatitis C virus is substantial threat to the public health and its frequency is increasing rapidly all over the world. Approximately 5 online databases are available related to Hepatitis C virus. All these databases mainly concerned their nati...