Publications

Anemia, a disease which is caused by an inadequacy of hemoglobin or red blood cells in the blood. It is very risky at the time of pregnancy, menstruation and in ICU sometimes causing death. So, it is a need of hemoglobin and detects anemia quickly. Usually, doctors examine the eye conjunctiva color and confirmed by a blood test which is painful, time-consuming and costly. In this study, a total of 104 people (54 males and 50 females) is collected with their clinical blood hemoglobin level, anemic condition and taken palpebral conjunctiva image. The images are captured with a cell phone camera of good resolution. By using the images, the percentage of the red, green and blue pixels are extracted in MATLAB, image processing method. Taking those features, the Hemoglobin level is plotted. A total of 81 data is taken for training purposes and 23 data for testing. For Anemia detection, the 81 data are trained with a used different classifier such as Linear SVM, Coarse Tree, and Cosine KNN and have been got highest accuracy of 82.61% in Decision Tree (Coarse) by testing 23 data.

Apart from all, in this study total, 104 data are collected from Chittagong Medical College Hospital, Chittagong and Cox’s Bazar Medical College Hospital, Bangladesh with the clinical anemic condition and palpebral conjunctiva image which contained 54 males and 50 females. All data are taken from the patient after taking their consent. Among them 25 people are anemic and 79 people are non-anemic. From the total dataset 81 data are taken to train the model and 23 data kept separate for further testing the system. Basically, the image of their eye conjunctiva is captured and applied image processing to extract the percentage of red, green and blue. The aim of this work is to predict anemia using different

Hemoglobin (Hb), a very significant parameter for the human body and deficiency of it causes anemia. During pregnancy, menstruation and ICU deficiency of it can be very risky and even caused death. So, it is important to diagnose it continuously. Usually, physicians examine it by conducting a blood test to confirm it is painful, time-consuming and costly. The major concept of this study is to screen Hb levels within a short period of time. In this study, the data of clinical blood Hb levels of a total of 104 people (54 males and 50 females) are collected along with an eye conjunctiva image. The images are taken with a Smartphone camera of constant resolution and lighting. Using MATLAB, image processing method, the percentages of the red, green and blue pixels are extracted. Taking those features, the Hb level is plotted. The 104 data have been split into two sets where the first 81 data for training purposes, the remaining 23 data have been considered for testing. To train the model of 81 data, Multivariate Linear Regression (MLR), Decision Tree (Medium), Linear Support Vector Regression (SVR) are taken and the lowest percentage of error of 11.01% has been found in the Decision Tree (Medium) while testing the 23-test data. We got serious error with those data that are extreme, like we got maximum error in measuring low hemoglobin level like 6.3 and less than that

With the expanding number of people diseases are additionally expanding. Alongside that with the climate, nourishment characteristics are having likewise sway on affliction. The more prominent number of individuals become debilitated, the more weight on emergency clinic, specialist's chamber and demonstrative focus. Along these lines, by the assistance of IoT this weight can be relieve in an extraordinary degree. There are some proficient sensors and processing unit that can quantify body parameters with a level of accuracy almost like genuine symptomatic focus can do. Plus, in the wake of estimating these body parameters the preparing units (Raspberry Pi, Arduino, Microcontroller, WeMos Lolin 32) can process that body parameters and anticipate anything dependent on the information. This paper is based on e-clinic services for the remote area. A system has been designed and implemented by which three important body parameter such as Body temperature, Heartrate and Blood oxygen saturation can be measured and uploaded to cloud using IoT and android app. These body parameters can be monitored remotely by using website or android app by experts. The whole system is more efficient and more cost effective than existing systems.