Expert lecture on “From pixels to diagnosis : Automated detection of Diabetic Retinopathy”

Event Date: 8th April 2025

Event brief description

The expert lecture on “From pixels to diagnosis : Automated detection of Diabetic Retinopathy” was scheduled on 8th April 2025 by Ms Anshul Khanna, Assistant Professor, School of Computer Application and Technology, Galgotias University. The event was attended by students and faculty members. The event showed the transition from raw retinal images to accurate diabetic retinopathy diagnoses which is the power of automated detection systems. By leveraging advanced image processing, machine learning, and deep learning techniques, these systems can assist clinicians in early detection, reduce diagnostic workload, and improve patient outcomes. As technology evolves, such tools hold the promise of making diabetic eye care more accessible, efficient, and accurate.

Event Detailed Description

The expert lecture on 'From Pixels to Diagnosis: Automated Detection of Diabetic Retinopathy' was successfully conducted, providing valuable insights into the integration of machine learning and deep learning techniques in medical image analysis. The lecture delivered by Ms Anshul Khanna, Assistant Professor, School of Computer Application and Technology, Galgotias University on 8th April 2025, provided valuable insights into the application of machine learning and deep learning techniques in the early detection of diabetic retinopathy using retinal images. The lecture highlighted the challenges in traditional screening methods and demonstrated how AI-powered solutions can significantly enhance accuracy, speed, and accessibility of diagnosis, especially in resource-limited settings. It was well-received by the audience, which included students and faculty members who appreciated the clarity, relevance, and depth of the presentation. The session concluded with an engaging Q&A segment, reflecting the participants’ strong interest in the topic and its real-world applications in healthcare.

Department Name –Department of Computer Application and Technology

Event Outcome : The lecture highlighted the challenges in traditional screening methods and demonstrated how AI-powered solutions can significantly enhance accuracy, speed, and accessibility of diagnosis. The event was attended by a diverse audience comprising students and faculty members. Participants found the session highly informative and inspiring. The lecture concluded with a lively interaction during the Q&A session, where thought-provoking questions were addressed, reinforcing the relevance and future potential of AI in medical diagnostics.