BREAST CANCER CLASSIFICATION SYSTEM

Abstract

ABSTRACT A breast cancer classification system (100), comprising: a multi-modal imaging module (110) configured to acquire medical imaging data from multiple modalities including mammography, MRI, and ultrasound; a machine learning module (120) integrated with the imaging module, the machine learning module configured to analyze the acquired medical imaging data, detect abnormal tissue patterns, and classify breast cancer into different stages; a control module (130) configured to manage the data flow and processing steps; a user interface (140) that provides real-time feedback and classification results to the clinician. <>

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of biomedical engineering and, in particular, to a breast cancer classification system utilizing machine learning algorithms to improve the accuracy, efficiency, and real-time capabilities of breast cancer diagnosis through the analysis of medical imaging data.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Breast cancer remains one of the leading causes of death among women worldwide, and early detection is critical for improving surv , Claims:CLAIMS
WE CLAIM:
1. A breast cancer classification system (100), comprising:
a multi-modal imaging module (110) configured to acquire medical imaging data from multiple modalities including mammography, MRI, and ultrasound;
a machine learning module (120) integrated with the imaging module, the machine learning module configured to analyze the acquired medical imaging data, detect abnormal tissue patterns, and classify breast cancer into different stages;
a control module (130) configured to manage the data flow and processing steps;
a user interface (140) that provides real-time feedback and classification results to the clinician.

2. The system (100) as claimed in claim 1, wherein the machine learning module (120) utilizes deep learning techniques, including convolutional neural networks (CNNs), to analyze the medical images.

3. The system (100) as claimed in claim 1, wherein the multi-modal imaging module (110) captures images from mammography, MRI, and ultrasound, allowing for a comprehensive analysis.

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