Mr. Damodar Reddy

Mr. C. Damodar Reddy

Managing Director

D.C Reddy

Prof. Dr . D. C.Reddy

Founder & Chief Advisor to Managing Director

Qualifications: B. Engg (Osmania Univeristy); Masters in Mfg Engineering (Wayne State Univeristy, US)

  • Experience: Previously worked in various capacities in technical & management consultancy roles catering to various industries including healthcare delivery space.
  • Role at Deccan: Actively involved in day-to-day operations, designing and implementation of management systems and quality improvement initiatives, ensuring smooth functioning of facilities.

Our Vision

Endeavour to emerge as the leading Healthcare Provider, in selective areas of Medical & Surgical Practice.

Our Mission

To Practice healthcare with compassion and Dedication while being resolute in implementing the best medical technology and procedures with expertise, aiming to reduce suffering and “Begin wellness”.

Prof. Dr.D.C Reddy, a distinguished alumnus of the Department of Electronics and Communication Engineering of the University College of Engineering, Osmania University (OU), Hyderabad, secured his B.E. degree in ECE from the same University. He obtained the MSEE degree from the University of Santa Clara, California and the Ph.D. degree from the University of Iowa, Iowa, where he also taught for a brief period. Subsequently, he moved to the University of Illinois, Chicago, to continue his teaching career. He returned to his alma mater to teach while pursuing his research interests in Control Theory. Nonlinear Systems, Biomedical Signal Processing and Navigational Electronics (GPS). He is the author/co-author of several research publications in the above mentioned areas. He has executed a number of Research and Development projects and these topics.

As a professor in the Department of Electronics and Communication Engineering, Osmania University, he was the Founder Head of the Department of Biomedical Engineering. He was the President of the Biomedical Engineering Society of India for over three Years.

While at Osmania University, he held several high academic positions ranging from the Head and Chairman of the Departments of ECE and Biomedical Engineering to the Dean, Faculty of Engineering to the Principal of the University College of Engineering (Autonomous). He retired as the Vice-Chancellor of Osmania University.

He was also the Director for the R and T Unit for Navigational Electronics, Osmania University for almost a decade.

He received many awards during his academic and teaching career, including the Vishishta Puraskaram award of the Dr. Ramineni Foundation, USA for his scholarship and academic leadership.

Carried out a collaborative research on historical patient data to evaluate the performance of Radiology Analysis Technology using AI sponsored by IBM, California – 2018.

Based on the above study published the article entitled.

A robust network architecture to detect normal chest

X-ray radiographs

Prof. Dr. D.C Reddy, Ken C. L. WongMehdi MoradiJoy WuAnup PillaiArjun SharmaYaniv GurHassan AhmadMinnekanti Sunil ChowdaryChiranjeevi JKiran Kumar Reddy PolakaVenkateswar WunnavaTanveer Syeda-Mahmood

We propose a novel deep neural network architecture for normalcy detection in chest X-ray images. This architecture treats the problem as fine-grained binary classification in which the normal cases are well-defined as a class while leaving all other cases in the broad class of abnormal. It employs several components that allow generalization and prevent overfitting across demographics. The model is trained and validated on a large public dataset of frontal chest X-ray images. It is then tested independently on images from a clinical institution of differing patient demographics using a three radiologist consensus for ground truth labeling. The model provides an area under ROC curve of 0.96 when tested on 1271 images. We can automatically remove nearly a third of disease-free chest X-ray screening images from the workflow, without introducing any false negatives (100% sensitivity to disease) thus raising the potential of expediting radiology workflows in hospitals in future.