METHOD FOR ENHANCING BREAST CANCER DIAGNOSIS

Abstract

ABSTRACT A method (100) for enhancing breast cancer diagnosis, comprising: acquiring medical images of a breast using one or more imaging modalities; preprocessing the acquired images to enhance quality and remove noise; applying machine learning algorithms to analyze the images and identify potential cancerous regions; classifying detected regions into benign or malignant categories; and providing diagnostic recommendations to the clinician based on the analysis results.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of medical diagnostics, specifically to a method for enhancing the diagnosis of breast cancer. The invention aims to improve the accuracy, efficiency, and reliability of breast cancer detection using advanced imaging technologies and machine learning techniques.
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] Early and accurate detection of breast cancer is critical for effective treatment and improved patient outcomes. Mammography, ult , Claims:WE CLAIM:
1. A method (100) for enhancing breast cancer diagnosis, comprising:
acquiring medical images of a breast using one or more imaging modalities;
preprocessing the acquired images to enhance quality and remove noise;
applying machine learning algorithms to analyze the images and identify potential cancerous regions;
classifying detected regions into benign or malignant categories;
providing diagnostic recommendations to the clinician based on the analysis results.

2. The method (100) as claimed in claim 1, wherein the imaging modalities include mammography, ultrasound, and MRI.

3. The method (100) as claimed in claim 1, wherein preprocessing the acquired images includes noise removal, contrast enhancement, and image resolution standardization.

4. The method (100) as claimed in claim 1, wherein applying machine learning algorithms includes training a model on a large dataset of labeled medical images.

5. The method (100) as claimed in claim 1, wherein classifying detected regions includes extr

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