Soft computing to determine a Hemoglobin level of an early stage Multiple Myeloma patient by using Rectified Linear Units (ReLu) activation function - Mishra, Akshansh; Diwan, Mitali (International Journal of Computer Sciences and Engineering, 2019)
Artificial Intelligence (AI) has found various applications in many industries, from development of new alloys to cyber security and healthcare domain. By 2025 it is expected that the market for healthcare artificial intelligence tools will surpass 34 billion dollars. There is no doubt that the application of AI is going to lead to a real digital shift in traditional medical imaging, requiring AI and people to work together to meet the challenges of the medical industry. In our present work, we have tried to determine the hemoglobin level corresponding to Packed Cell Volume (PCV) and Red Blood Cells (RBC) count. In the Artificial Neural Network (ANN) architecture, PCV (%) and RBC count (mill/cumm) are the inputs while hemoglobin (g/dL) is the output. The result obtained is quite promising. Artificial Neural Network (ANN) trained on Rectified Linear Unit (ReLu) activation function showed 97.15% accuracy.