Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Electronics

Giovanni Dimauro¹  Lorenzo Simone²  Thulasi Bai Vijayan³  Sivachandar Kasiviswanathan³ 

[1] Department of Computer Science, University of Bari, 70125 Bari, Italy;Department of Computer Science, University of Pisa, 56127 Pisa, Italy;Department of Electronics and Communication Engineering, KCG College of Technology, Chennai 600097, India;

关键词: convolutional neural network (CNN);    bio-medical applications;    deep learning;    computer-aided diagnostics;    image processing;   

DOI  :  10.3390/electronics9081309

来源: DOAJ

【 摘 要 】

Technology is changing the future of healthcare, technology-supported non-invasive medical procedures are more preferable in the medical diagnosis. Anemia is one of the widespread diseases affecting the wellbeing of individuals around the world especially childbearing age women and children and addressing this issue with the advanced technology will reduce the prevalence in large numbers. The objective of this work is to perform segmentation of the conjunctiva region for non-invasive anemia detection applications using deep learning. The proposed U-Net Based Conjunctiva Segmentation Model (UNBCSM) uses fine-tuned U-Net architecture for effective semantic segmentation of conjunctiva from the digital eye images captured by consumer-grade cameras in an uncontrolled environment. The ground truth for this supervised learning was given as Pascal masks obtained by manual selection of conjunctiva pixels. Image augmentation and pre-processing was performed to increase the data size and the performance of the model. UNBCSM showed good segmentation results and exhibited a comparable value of Intersection over Union (IoU) score between the ground truth and the segmented mask of 96% and 85.7% for training and validation, respectively.

【 授权许可】

Unknown