Haroon Ahmed Khan's research while affiliated with COMSATS University Islamabad and other places
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Publications (8)
The precise segmentation of skin lesion in dermoscopic images is essential for the early detection of skin cancer. However, the irregular shapes of the lesions, the absence of sharp edges, the existence of artifacts like hair follicles, and marker color make this task difficult. Currently, fully connected networks (FCNs) and U-Nets are the most com...
Efficient Waste management plays a crucial role to ensure clean and green environment in the smart cities. This study investigates the critical role of efficient trash classification in achieving sustainable solid waste management within smart city environments. We conduct a comparative analysis of various trash classification methods utilizing dee...
Internet of Medical Things (IoMT) can be leveraged for periodic sensing and recording of different health parameters using sensors, wireless communications, and computation platforms. Health care systems can be enhanced by using IoMT for remote patient monitoring and data-driven diagnosis powered by machine learning algorithms. In the context of Io...
In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual block-based convolutional neural network tailored for medical image segmentation on IoT and edge platforms. Conventional U-Net-based models face challenges in meeting the speed and efficiency demands of real-time clinical applications, such as disease monitoring, radiation...
Glaucoma is an eye disease that causes damage to the optic nerve, which can lead to visual loss and permanent blindness. Early glaucoma detection is therefore critical in order to avoid permanent blindness. The estimation of the cup-to-disc ratio (CDR) during an examination of the optical disc (OD) is used for the diagnosis of glaucoma. In this pap...
This paper presents LUVS-Net, which is a lightweight convolutional network for retinal vessel segmentation in fundus images that is designed for resource-constrained devices that are typically unable to meet the computational requirements of large neural networks. The computational challenges arise due to low-quality retinal images, wide variance i...
Manual assessment of biomedical imaging based diagnostics is limited as it is time-consuming and subjective. Bio-inspired diagnostics applications on embedded and mobile devices are becoming more popular as they overcome these limitations and aid in early detection and diagnosis. The neural theory of visual attention puts forward that the processin...
The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences in people’s physical and mental wellbeing. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension amongst the general populace globally. Protracted exposure...
Citations
... In [10], the authors present a feature enhancement segmentation network that alleviates the need for pre-training image enhancement, reducing associated computational overhead. The authors of [11], [12] and [13] build networks with a restricted number of trainable parameters, tailored for devices with limited resources. Although MobileNet-V3 [13] excels in object segmentation, it is not optimised for medical image segmentation. ...
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... Deep learning methods [22], such as convolutional neural networks (CNNs), have been successfully applied to image segmentation tasks in various domains [23,24], including natural images, satellite images, and medical images, achieving remarkable results. Islam et al. [25] proposed a lightweight convolutional network, LUVS-Net. The network utilizes an encoder-decoder framework, wherein edge data are transposed from the first layer of the encoder to the last layer of the decoder, significantly improving the convergence speed. ...
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... The presence of exudates, lesions, and hemorrhages can further complicate the task at hand. Many studies for automatic segmentation of vessels by means of computer vision with either supervised or unsupervised algorithms [11,12,13,14,15,16,17,18] have been reported. Studies using deep learning architectures have, in particular, been found to be more effective than alternatives [19,20,21]. ...
... In [45], used PSS for identifying stress in university students during COVID-19. [50], developed a secure framework that utilizes wearable sensors for measuring and transmitting physiological signals to a cloud-based server, which then uses machine learning to predict stress levels. ...
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