Dr Mohammed Shafeeq Ahmed
@gug.ac.in
Lecturer, Department of Computer Science
Gulbarga University, Gulbarga
EDUCATION
I received the B.Sc. Computer Science in 2006, from Gulbarga University, Gulbarga, M.C.A. in 2009, from Gulbarga University, Gulbarga, and has awarded Ph.D. in 2022, from Bharathiar University, Coimbatore, India.
RESEARCH INTERESTS
Image Processing, Computer Network, Cyber Security, Software Engineering, Big Data, Data Mining & AI.
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Mohammed Shafeeq Ahmed, Tiruvedula Mithun, Regonda Nagaraju, Y Sowmya Reddy, Walunj Madhukar Baban, and Sushma Jaiswal
IEEE
Patients with Diabetic Retinopathy (DR) who do not receive appropriate diagnosis and treatment may go blind as the disease affects the retina's blood vessels. Diabetic retinopathy disease must be detected and diagnosed early if the patient's vision is to be preserved. Specifically, this research focuses on digital image analysis, which has huge benefits in terms of both time and money savings, as well as giving more objective measurements than current observer-driven systems and decreasing the workload required from manually taught graders.
Mohammed Shafeeq Ahmed and Baddam Indira
IEEE
Diabetic Retinopathy (DR) is characterized by color based pathologies. The fundus camera generates fundus image in RGB color and ophthalmologist analyze that image and mark the pathologies to diagnose level of DR. Early detection of exudates from retinal images can potentially reduces the risk of blindness of people suffering from DR. The aim of the work presented in this paper is to detect the exudates (yellowish fat deposit on the retinal surface) from fundus images in RGB color space, thereby facilitate a realistic diagnosis close to the method adopted by ophthalmologist. A Statistical measure-three sigma is used to compute the color intensity range of exudates pixels. The retinal images are preprocessed to enhance the color intensity and optic disk (OD) is eliminated because, it shares similar features with exudates. The aim and objective of this paper is to detect the exudates from RGB fundus images, the pre-processed images are then classified based on the information extracted from three-sigma control method. The results so obtained are promising and also facilitates the ophthalmologist in diagnosing the disease.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
1. Detection and Classification of Diabetic Retinopathy from Fundus Images using Optimized 3 Sigma and NN. (Application No. 202241000469, Journal No. 05/2022, pp. no 6187, dated 04/02/2022).
2. Auto Encoder Deep Convolutional Neural Network and Machine Learning Approaches for Image Retrieval System. (Application No. 202111055040, Journal No. 49/2021, pp. no 57834, dated 03/12/2021).
3. Reliability Control using Loss Recovery Techniques in Wireless Sensor Network. (Application No. 202121052948, Journal No. 48/2021, pp. no 56292, dated 26/11/2021).