Research & Projects

Introduction

In the ever-evolving landscape of modern medicine, robotic systems have emerged as pioneers, reshaping surgical practices worldwide. Their remarkable precision and minimally invasive techniques have revolutionized the healthcare industry. One crucial aspect behind their success is the ability to accurately identify and segment surgical tools within complex and diverse surgical scenarios. This task, however, presents unique challenges. In this portfolio, I present a groundbreaking deep learning architecture called the Robotic Surgical Tool Segmentation Network, or RSSNet. RSSNet has been meticulously designed to elevate the accuracy of surgical tool segmentation in robotic surgeries.

Advantage

Our proposed network incorporates an innovative fusion of techniques to achieve unparalleled accuracy in surgical tool segmentation. It harnesses the strength of Atrous Spatial Pyramid Pooling in combination with efficient average pooling layers to extract and encode intricate details, ensuring precise segmentation. What sets RSSNet apart is its fine-tuning strategy on the average pooling layers, specifically tailored to enhance performance in handling small and intricate details. Through rigorous testing on primary datasets, including Kvasir and EndoVis 2017, as well as additional surgical datasets, RSSNet has demonstrated unwavering robustness and adaptability.

Outperformance

Our proposed model consistently emerges as the frontrunner in head-to-head comparisons with state-of-the-art models. Its prowess in surgical tool segmentation was established beyond doubt. An ablation study further reaffirmed the effectiveness of each component in the model architecture, with particular emphasis on the fine-tuned average pooling layers and the core module.

Paving the Way for Autonomous Robotic Surgeries

The significance of this study extends far beyond the realm of research. Our model represents a novel, efficient, high-performing surgical tool segmentation solution. It marks a pivotal step toward the realization of fully autonomous robotic surgeries, where precision and reliability are paramount. With a remarkable performance score of 96.78%, this work sets the stage for future optimization strategies and the integration of additional data modalities to enhance the performance of RSSNet further.

Research Article:

Muhammad Sadaqat Janjua , Hussam Ali , Qaisar Farooq . RSSNet: A Fine-tuned Deep Learning Network for Robotic Surgical-tool Segmentation. TechRxiv. November 07, 2023.

Introduction

In today's fast-paced world, traffic accidents have become an unfortunate norm across the globe. These incidents can often be attributed to one alarming factor: driver drowsiness. As drivers embark on long journeys, the comfort of modern vehicles can inadvertently induce relaxation, ultimately leading to drowsiness and, in some cases, even sleepiness. These conditions dramatically increase the risk of road accidents. This portfolio showcases a groundbreaking innovation: an Artificial Intelligence-based Driver Drowsiness Application. Our mission is to harness the power of AI to enhance road safety by preventing accidents caused by driver fatigue.

The Need for Vigilance

In our modern era, where convenience and comfort are paramount in vehicle design, addressing the unintended consequence of increased drowsiness during long journeys is essential. Our research acknowledges this challenge and strives to mitigate its consequences.

A Smart Solution

We introduce a state-of-the-art, intelligent, mobile-edge-based drowsiness detection system. This system leverages Google Vision's cutting-edge image processing capabilities to detect crucial facial cues and eye aspect ratios on mobile devices. When signs of drowsiness are detected, the system promptly alerts the driver, effectively averting potential accidents.

Processing at the Edge

Our innovative approach processes all data directly on the edge device, ensuring real-time detection and response. This efficient edge computing strategy maximizes the system's responsiveness and minimizes latency, providing drivers with the utmost safety.

A Commitment to Preventing Accidents

Our research represents a significant stride towards ensuring road safety. By proactively addressing the issue of driver drowsiness through advanced AI-driven technology, we aim to save lives and prevent countless accidents.

Research Article:

Janjua, M., Safdar, I., Jamil, B., & Ijaz, H. (2024). A Mobile-Edge-Based Smart Driver Drowsiness Detection System. Technical Journal, 29(01). Retrieved from https://tj.uettaxila.edu.pk/index.php/technical-journal/article/view/1910

As a dedicated reviewer, I have had the privilege of contributing to the advancement of computer systems research and innovation. My expertise in the field allows me to meticulously assess and provide constructive feedback on cutting-edge research papers, ensuring the highest standards of quality and relevance for the journal's readership.

My journey as a reviewer in "Future Generation Computer Systems" has been marked by a commitment to excellence, a passion for staying at the forefront of emerging technologies, and a genuine interest in fostering the growth of the scientific community. I take great pride in being part of this dynamic and influential platform that shapes the future of computer systems and technology.

With a strong passion for cutting-edge technology and a commitment to advancing the future of computer systems, I have established myself as a recognized authority in the industry. Through my rigorous review process, I have contributed to the publication of numerous high-quality research papers that shape the field's advancements.