PREDICTION OF BLOOD CANCER USING EMSEMBLE LEARNING TECHNIQUE
Abstract
ABSTRACT
Blood cancer remains a leading cause of mortality worldwide. Accurate and early detection is essential for effective treatment. This research focuses on predicting blood cancer using a deep learning approach integrated with ensemble learning techniques. A custom Kaggle dataset containing 2,478 cancerous and 2,483 normal blood smear images was used. The methodology involved preprocessing images, implementing a Convolutional Neural Network (CNN) in TensorFlow.js, and applying ensemble learning techniques such as stacking and boosting to improve performance. The model achieved an accuracy of 99.9%, demonstrating the potential of deep learning in medical diagnostics.
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