Implementation towards Blood Cancer Detection with Convolutional Neural Network

Abstract

This Blood cancer is a life-threatening disease that requires early and accurate detection for effective treatment. In this project, we present a novel approach for blood cancer detection using a Convolutional Neural Network (CNN) model. The CNN model is trained on a dataset comprising cancer and normal blood cell images. Through extensive analysis and evaluation, we achieve a high level of accuracy in distinguishing between cancerous and normal blood cells. To evaluate the performance of our model, we conducted tests on a separate set of cancer and normal blood cell images. The accuracy of our model was determined by comparing the predicted labels with the ground truth labels. The results demonstrate that our CNN model achieves a commendable accuracy rate, making it a promising tool for blood cancer detection. Furthermore, we discuss the significance of our findings and their potential implications for early diagnosis and improved treatment outcomes. The robustness and reliability of our model contribute to its practical utility in clinical settings. By enabling early detection of blood cancer, our approach has the potential to positively impact patient outcomes and enhance the efficiency of treatment strategies. In conclusion, this project presents a novel approach for blood cancer detection using a CNN model. The results demonstrate the effectiveness of our model in accurately distinguishing between cancerous and normal blood cells. The proposed method holds promise for improving blood cancer diagnosis and ultimately contributing to better patient care.