An Effective Convolutional Neural Network for Identifying Cancer Blood Disorder Cells Using Microscopic Images

Blood, bone marrow, and lymphatic systems are all impacted by hematological cancer is known as a
cancer blood disorder. Blood malignancies and various blood disorders pose significant health
challenges across all age groups. Early disease detection is essential for effective cancer blood disorder
treatment and management. If a blood cancer is not identified in time, it may be hazardous. It results
in abnormal white blood cell production by the bone marrow in the blood. It is possible to diagnose
blood cancer early with deep learning algorithms. Our study presents a novel and highly effective
approach to predicting cancer blood disorders from medical images. The convolutional neural
networks (CNN) method will be used to extract characteristics from blood samples or images, followed
by deep learning algorithms to separate malignant from non-cancerous samples. Many researchers
have proposed various deep learning-based techniques to improve the accuracy of blood cancer
diagnosis, such as feature selection and hybrid models. Our revolutionary DCNN classification
architecture trains quickly. With 98% accuracy, our method is incredibly successful. To compare our
system to existing classifiers to test its performance. We developed a complete system for segmenting
and predicting cancer-related blood abnormalities, exceeding current methods. Based on the results,
deep learning approaches have the potential to enhance blood cancer diagnosis and therapy by
achieving high detection accuracy. The study also highlights this field's future directions. However
further study is required to create more accurate and reliable models for therapeutic use.