Automatic Chest X-rays Analysis using Statistical Machine Learning Strategies

Tuberculosis (TB) is a disease responsible for the deaths of more than one million people worldwide every year. Even though it is preventable and curable, it remains a major threat to humanity that needs to be taken care of. It is often diagnosed in developed countries using approaches such as sputum smear microscopy and culture methods. However, since these approaches are rather expensive, they are not commonly used in poor regions of the globe such as India, Africa, and Bangladesh. Instead, the well known and affordable chest x-ray (CXR) interpretation by radiologists is the technique employed in those places. Nevertheless, if this method is obsolete in other parts of the world nowadays it is because of its many flaws including: i) it is a tedious task that requires experienced medical personnel --which is scarce given the high demand for it--, ii) it is manual and difficult when executed for a large population, and iii) it is prone to human error depending on the proficiency and aptitude of the interpreter. Researchers have thus been trying to overcome these challenges over the years by proposing software solutions that mainly involve computer vision, artificial intelligence, and machine learning. The problems with these existing solutions are that they are either complex or not reliable enough. The need for better solutions in this specific domain as well as my desire to bring my contribution to something meaningful are what led us to investigate in this direction. In this manuscript, I propose a simple fully automatic software solution that uses only machine learning and image processing to analyze and detect anomalies related to TB in CXR scans. My system starts by extracting the region of interest from the incoming images, then performs a computationally inexpensive yet efficient feature extraction that involves edge detection using Laplacian of Gaussian and positional information retention. The extracted features are then fed to a regular random forest classifier for discrimination. I tested the system on two benchmark data collections --Montgomery and Shenzhen-- and obtained state-of-the-art results that reach up to 97% classification accuracy

Sentiment Analysis Using Machine Learning

Sentiment analysis is the process of computationally evaluating spoken or written language to determine if the message that is being conveyed yields a positive, negative or neutral opinion. It is really important because nowadays, companies receive a massive amount of reviews about their products, brands, websites, customer service etc... and the way this information is handled can be critical for their success. They therefore, must find a way to process this information quickly and accurately so that they can respond to customers’ needs on time. Having this in mind, we developed a software using machine learning tools, that is able to predict if a text will have a positive or negative impact on the reader. We used the TF-IDF (term’s frequency-Inverse Document frequency) technique to pre-process the input text (convert it to numbers) and the MLP (multilayer-perceptron) to classify the reviews. We obtained a sample of 25000 movie reviews from the IMDB website, trained our model using 75% of them and tested it with the rest(25%). Following this process, we were able to correctly classify 91% of the reviews that were used for testing. The software is written in Python using the machine learning library ”Scikit-Learn.” It is ready to be used and is able to classify a large amount of reviews in a short period of time. Winner, Outstanding Oral Presentation, School of Graduate Studies and Research