TF LITE BASED BLOOD CANCER CLASSIFICATION SYSTEM

TF LITE BASED BLOOD CANCER CLASSIFICATION SYSTEM

 VEERESH K C 1, SINDHU S L 2

1 STUDENT DEPARTMENT OF MCA, BIET, DAVANGERE

2ASSISTANT PROFESSOR, DEPARTMENT OF MCA BIET DAVANGERE

ABSTRACT

Blood cancer, comprising various hematologic malignancies such as leukemia, lymphoma, and myeloma, presents significant diagnostic challenges that require precise and timely identification for effective treatment. The TF-Based Blood Cancer Classification System leverages the capabilities of TensorFlow (TF) to develop a robust, scalable, and accurate machine learning model for classifying different types of blood cancer from medical imaging and genetic data. This system integrates advanced deep learning techniques, including convolutional neural networks (CNNs) for image analysis and recurrent neural networks (RNNs) for genetic sequence analysis, to process and interpret complex biomedical data. The proposed system employs a comprehensive dataset containing labeled images and genetic profiles of various blood cancer types. Data preprocessing steps, such as normalization and augmentation, are applied to enhance model performance and generalization. The classification model is trained and validated using TensorFlow’s high-performance computing capabilities, achieving high accuracy and reliability in distinguishing between different blood cancer subtypes. Initial results demonstrate the system's potential to significantly improve diagnostic accuracy and speed, outperforming traditional diagnostic methods. This advancement not only aids healthcare professionals in making informed decisions but also contributes to personalized treatment strategies. The TF-Based Blood Cancer Classification System represents a significant step towards integrating artificial intelligence in medical diagnostics, offering promising prospects for improving patient outcomes in hematologic oncology.

Keywords: Blood cancer, TensorFlow, machine learning, deep learning, convolutional neural networks, recurrent neural networks, medical imaging, genetic data, diagnostics, hematologic malignancies.