Deep Learning Model Design for Blood Cancer Prediction through AI-Driven Strategies
A. Hency Juliet · 2024
The study seeks to create and utilize sophisticated deep learning model namely Convolutional Neural Network (CNN) designed for image classification purposes. It’s a custom CNN architecture tailored for the task, to classify images of blood cells into eight distinct categories. It comprises several layers, starting with convolutional layers followed by max-pooling layers, and fully connected layers empowered by AI technique, aiming to precisely forecast different forms of blood cancer using a dataset comprising images of blood cells. Ensuring the consistency and quality of input data, especially in medical imaging datasets susceptible to variations affecting model performance, poses a significant challenge; improving the interpretability and explainability of deep learning models is crucial for their reliable incorporation into clinical decision-making processes for blood cancer prediction. Utilize sophisticated deep learning model design methodologies guided by Artificial Intelligence approaches, tailored to address the unique characteristics of the blood cell image dataset, in order to surmount the outlined challenges. The suggested system provides improved precision and effectiveness in predicting blood cancer by employing advanced deep learning models guided by customized Artificial Intelligence techniques. Throughout the span of 50 epochs, the model’s performance was assessed based on its training and validation accuracy, gradually enhancing until reaching final rates of about 90.31% for training and 90% for validation.