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The detection of anemia from anemic retinopathy involves the identification and analysis of specific retinal changes that occur as a result of severe anemia which requires analysis of multimodal sources. Existing models for identification of these conditions either showcase high complexity, or have lower efficiency when evaluated on clinical scans. Moreover, most of these models cannot be scaled for multi-disease images, which limits their applicability under real-time use cases. To overcome these issues, this research article proposes the design of an efficient and novel multidomain feature analysis model for the estimation of anemic conditions with the help of deep transfer learning process. The proposed model initially represents retinal image scans into multiple domains via estimation of Frequency, Gabor, Convolution, Wavelet and Entropy features. These features are selected via Bacterial Foraging Optimizer (BFO), which assists in retaining high variance feature sets. Due to this, the model is able to reduce redundancies, that assists in improving the speed and accuracy of classification operations. To perform this task, the model uses a set of customized 1-D Cascaded Binary CNN, that assist in categorizing input images into 4 different classes. These include, “Anemic due to Glaucoma”, “Glaucoma”, “Anemic”, and “Normal”, with 98.3% accuracy which is 4.5% higher than existing methods. The model is also able to improve the precision by 2.9%, recall by 3.5%, while reducing the delay needed for classification by 5.9% when compared with existing techniques. Due to which, the model is highly useful for clinical deployments. [ABSTRACT FROM AUTHOR]

Anemia, generally termed as deficiency of hemoglobin or red blood cells in the blood is significant global health concern for the population in underdeveloped as well as in developing nations specially, for children and young women in rural areas. This paper proposes a quantitative approach for anemia detection by regression analysis technique which predicts hemoglobin level in the blood. To achieve this, the image dataset of microscopic blood sample is collected from 70 individuals. The data collection requires proper procedure as it plays vital part in system implementation. The statistical feature utilizing mean pixel intensity values from the red, green, and blue color planes of the images are given as input to the regression model. For the proposed system, we have employed multiple regression analysis model using machine learning approach with both three and four regression coefficients to establish relation between features obtained from blood samples and the hemoglobin level in the blood to achieve the specified task of anemia detection in an individual. Performance analysis show promising results for the proposed system with coefficient of determination (R²) and root mean square error (RMSE) found out be 0.923 and 1.682 respectively. Overall, this paper presents valuable system for anemia detection based on hemoglobin estimation which can be implemented in areas with limited medical resources and gives another supportive technological solution for current healthcare problems. [ABSTRACT FROM AUTHOR]