dc.description.abstract |
Radiology Image Analysis is a critical sector and this job mostly being done by medical
specialists and people expect the highest level of care and service regardless of cost. Due to
the complexity and subjectivity of images, it is limited. Widespread variation exists across different
interpreters and labor in terms of image interpretation by human experts. My objective is to
analyze medical X-ray images using deep learning and utilize images using Pandas, Keras,
OpenCV, Tensorflow, etc to obtain a classification of images like Atelectasis, Consolidation,
Cardiomegaly, Edema, Effusion, Emphysema, Fibrosis, Hernia, Infiltration, Mass, Nodule,
Pleural, Pneumonia, Pneumothorax, Thickening, etc. I have used Convolution Neural
Networks(CNN) algorithm because compared to other image classification algorithms CNN
have the ability to automatically extract high-level representations from big data using little
pre-processing. Ultimately, a simple and efficient model will lead clinicians towards the better
diagnostic decision for patients to provide them solutions with good accuracy. |
en_US |