CLASSIFICATION OF IMAGES BASED ON PIXELS THAT REPRESENT A SMALL PART OF THE SCENE. A CASE APPLIED TO MICROANEURYSMS IN FUNDUS RETINA IMAGES

Convolutional Neural Networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting Referable Diabetic Retinopathy (RDR). Having a size of less than 1% of the total image, microaneurysms are early lesions in DR that are difficult to classify. The purpose of this thesis is to improve the accuracy of detection of microaneurysms using a model that includes two CNNs with different input image sizes, 60x60 and 420x420 pixels. These models were trained using the Kaggle and Messidor datasets and tested independently against the Kaggle dataset, showing a sensitivity of 95% and 91%, a specificity of 98% and 93%, and an area under the Receiver Operating Characteristics curve of 0.98 and 0.96, respectively, in the sliced images. Furthermore, by combining these trained models, there was a reduction of false positives for complete images by about 50% and a sensitivity of 96% when tested against the DIARETDB1 dataset . In addition, a powerful image pre-processing procedure was implemented, which included adjusting luminescence and color reduction, improving not only images for annotations, but also decreasing the number of epochs during training. Finally, a novel feedback operation that re-sent batches not classified as well as expected, increased the accuracy of the CNN 420 x 420 pixel input model. CLASSIFICATION OF IMAGES BASED ON PIXELS THAT REPRESENT A SMALL PART OF THE SCENE. A CASE APPLIED TO MICROANEURYSMS IN FUNDUS RETINA IMAGES A Thesis Presented to Department of Computer Science By Pablo F. Ordóñez Submitted in Partial Fulfillment of the Requirements for the Degree Master of Science, Computer Science Advisor: Jose Garrido KENNESAW STATE UNIVERSITY May, 2017 This work is dedicated to the people that are dear to my heart. First I’d like to dedicate this to my father Pablo Emiro Ordóñez, in his memory. He taught me the value of knowledge and fasctination for undiscovered phenomenas. Secondly, my wife, Jacqueline Giraldo, whose unconditional love and support have alleviated my difficult moments and kept me pursuing my dreams. Finally, my daughter, Victoria Ordóñez, who is my inspiration and my greatest motive of happiness. Her sharp humor and acute critical sense of thinking enchant and brighten any of my dark days. Utopia is on the horizon. I move two steps closer; it moves two steps further away. I walk another ten steps and the horizon runs ten steps further away. As much as I may walk, I’ll never reach it. So what’s the point of utopia? The point is this: to keep walking. Eduardo Galeano ACKNOWLEDGEMENTS I would like to thank Dr. Jose Garrido who rescued and valued our work. His door was always open to listen to my problems and to provide me with wise advice. My appreciation for you is in my mind and soul forever. I would also like to thank Dr. Sumit Chakravarty. His contributions and feedback to this project were invaluable and his extensive knowledge in the field was fundamental to find the right direction for which this study was headed. In addition, his dedication to this work exceeded any student imagination, and his willingness to improve our work encouraged me to give it my best effort. I want to give a special thanks to Dr. Dan Lo, who facilitated and mediated the final phase of this work. Also, I would like to express my gratitude to my friend and ”brother”, Carlos Cepeda, who seeded the love for math in my brain. We spent long nights decoding the complexity of this science and were vigilant of each other researches. I want to thanks to Dr Chih-Cheng Hung who taught me the alphabet of image processing and let me be a part of his lab for some time. I would like to express my deep appreciation for the Dabney family (Joseph, Susanne, Earl, Geneva, Scott, Mark, Chris), who embrace me as their own family member and were practically my second family. Finally, I want to express my very profound gratitude and respect to my family in Colombia, my mother Mery and my brother Wilson Renier who nurtured me in my early years. x List of Tables 2.1 PRATT’S CONFUSION MATRIX . 5 2.2 ALGORITHM PERFORMANCE . 5 4.1 KAGGLE RAW DATABASE . 20 4.2 STUDY DATABASE . 20 5.1 TRAIN IMAGES STATISTICS . 23 5.2 CENTROIDS L* CHANNEL . 26 5.3 BACKGROUND & VESSELS PIXELS VALUES . 29 5.4 DATASET A . 31 5.5 DATASET B . 31 5.6 DATASET C . 31 5.7 MODELS 60X60 . 32 5.8 MODELS 420X420 . 36 5.9 DROPOUT SETTING . 37 6.1 RAW VS PRE-PROCESSED IMAGES FOR MODEL A & B ON 420 × 420 SET . 40 6.2 RAW VS PRE-PROCESSED IMAGES FOR MODEL A & B ON 60 × 60 SET 41 xi 6.3 FEEDBACK VS INCREASING DROPOUT TRAINING SET . 43 6.4 FEEBACK VS INVREASING DROPOUT TESTING SET . 44 6.5 INPUT AUGMENTATION VS NEW INPUT SENSITIVITY & SPECIFICITY . 46 6.6 FINAL INPUT SENSITIVITY & SPECIFICITY . 47 6.7 ROC CUTOFF . 49 6.8 DIARETDB1 INPUT . 50