Pulla Sujarani,
M. Yogeshwari,
- Research Scholar Department of computer Science, Vels Institute of Science, Technology & Advanced Studies (VISTAS) India
- Assistant Professor Department of computer Science, Vels Institute of Science, Technology & Advanced Studies (VISTAS) India
Abstract
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.
Keywords: Blood Disorder, Deep Learning Techniques, Blood Sample Images, Classification, Convolutional Neural Network.
[This article belongs to Research & Reviews: Journal of Oncology and Hematology(rrjooh)]
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Research & Reviews: Journal of Oncology and Hematology
| Volume | 13 |
| Issue | 02 |
| Received | April 12, 2024 |
| Accepted | April 27, 2024 |
| Published | August 23, 2024 |