Abstract:
The amount of brain tumor patients are increasing indescribably in the recent years and it has
become a dangerous problem. For both men and women, brain tumor placed in 10th position of
the leading cause of death. If it is possible to detect any disease before it started to damage, the
chance of recovery from diseases gets the increase. Previous state-of-the-art techniques based on
magnetic resonance images (MRI), provide fast and robust detection of tumor on the brain. But
due to use of MRI images the complexity of these techniques become high. All these existing
techniques classify the MRI images and detect the brain tumor, which is computationally
expensive. We have proposed a stochastic method for automatic detection of brain tumor using
Internet of Things (IoT). The proficiency of the method is scrumptious, because it measures the
probability of brain tumor from our daily activities. In our experiment, we have used a portable
wrist wearable device and two extra sensors, which can track our daily activities. Some common
symptoms of brain tumor are used here to detect the brain tumor. We have proposed some
equations, which can easily measure these symptoms from our daily activities. Experimental
result for brain tumor patients‘ group and normal persons‘ group show the ability of the proposed
technique in automatic detection of brain tumor. In this paper, we show the effectiveness of our
method in automatic detection of brain tumor is demonstrated and it produces a better accuracy
with the comparison to previous state-of-the-art brain tumor detection techniques.
Description:
This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh.