Published on 02, Aug 2023

Ajay Reddy Yeruva Discusses Machine Learning in the Healthcare Industry

Ajay Reddy Yeruva has almost 10 years of experience as an IT specialist, providing considerable expertise and understanding of the industry to each project he works on. While he is presently employed as a Senior Software Engineer with the IP-DevOps team at Ritchie Bros. Auctioneers, he has been consistently contributing to his chosen profession and accumulating information that will lead to the creation of new technologies and innovations as an Independent Researcher and Observability Subject Matter Expert using emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Internet of Things (IOT).

One industry that has benefited from Ajay Reddy Yeruva’s independent research work is the Healthcare industry. As an Observability Subject Matter Expert, with a heavy impact across multiple healthcare sectors, Ajay Reddy Yeruva has discussed how Machine Learning can benefit medical and healthcare workers when it comes to the treatment of certain diseases. He goes into detail in a research article that he participated on, regarding Leukemia and the utilization of Machine Learning techniques for improving Observability to detect whether a patient has the condition much quicker than the current traditional methods.

Leukemia is a type of cancer in blood which impacts the lymphatic framework and the bone marrow and also impacts white blood cells. Leukemia, in contrast to other types of cancer, does not produce solid tumors; instead, it produces a huge number of aberrant white blood cells that crowd out the healthy blood cells.

Machine Learning algorithms can be widely utilized in the treatment of leukemia, whether it is to classify the various forms of leukemia or to determine whether a patient has the disease,” the article states. “It is a malignant kind of cancer that results in a number of medical issues. Expert hematologists and pathologists manually examine blood samples under the microscope to make a diagnosis. Techniques like image processing and pattern recognition can be utilized to help these experts. In order to attain excellent performance in the categorization of malignant leukocytes challenge, this paper suggests straightforward modifications to conventional neural network topologies.”

Leukemia can be automatically detected using computer-aided diagnostic (CAD) models, which can help doctors and be useful for leukemia early identification. In a single-center study, Ajay Reddy Yeruva and the team working on the documentation attempted to develop a Deep Learning model for classification of leukemic B-lymphoblasts. Data augmentation methods were utilized to manage the little dataset size and a Deep Learning technique was utilized to accelerate learning and upgrade the presentation of the recommended network in order to create a trustworthy and accurate deep learner.

The outcomes demonstrate that their suggested approach surpassed separate networks with a test accuracy of 95.59% in the Leukemic B-lymphoblast examination and was capable of merging characteristics extracted from the top deep learning models. The utilization of Machine Learning techniques, especially Deep Learning, in CAD frameworks, Whole Slide Imaging (WSI), and even applications and programming at hematology labs to help pathologists and oncologists in better identifying leukemia can be an expected future course for research.

For more information regarding Ajay Reddy Yeruva and his Independent Research work within the Machine Learning (ML) and  Artificial Intelligence(AI) developments for improving Observability of diversified research areas, see his recent publications.