Abstract:
Medical image analysis field is highly dependent on good quality research that
can result in time, cost improvements and aid in providing faster and better diagnosis
for patients. Machine learning and especially convolution neural networks has proven
to efficiently achieve the previously mentioned improvements in various medical field
tasks. In this research we will focus on classification of cells based on their health
level using a CNN model and several image preprocessing techniques with the goal of
achieving high accuracy levels of predictions. The dataset used in this study has more
than 20000 images for training and will be tested on two different datasets with each
more than 8000 images. Several preprocessing techniques such as Wavelet denoising,
Sobel filter, sharpening and edge enhancing filters will be tested and compared based
on performance during the classification tasks with graphs and numerical results. The
modified CNN model will be tested to find out the best parameters to use for training
it and efficiently increasing the performance and precision.
