AI Model for Classifying Breast Cancer From Histopathology Images

Summary

Breast cancer, a prevalent and potentially fatal disease, underscores the need for early and accurate detection to improve patient outcomes. Traditional histopathological examination, the current gold standard for diagnosis, faces limitations like subjectivity and low efficiency. In response, this research seeks to revolutionize breast cancer diagnostics by using deep learning techniques to classify invasive and noninvasive breast cancer types from histopathological images. Non-invasive cancers, like DCIS and LCIS, are confined to milk ducts or lobules, while invasive cancers spread to surrounding tissue and make up 70% of cases, often leading to poorer outcomes. The proposed AI model aims to enhance diagnostic accuracy and efficiency, surpassing manual methods, and providing a scalable solution for diverse healthcare settings. By automating image analysis, the model seeks to democratize cancer screening, making it accessible in underserved populations and adaptable to different resources and equipment. Ultimately, this research aims to advance breast cancer detection, improve patient care, and contribute to better treatment outcomes globally.

Eligibility

* Inclusion criteria:

  * Female patients of any age can be selected as subjects.
  * Individuals willing to participate in breast cancer screening.
  * Availability for biopsy examination.
  * Women with no current or prior diagnosis of breast cancer.
  * Availability of relevant medical records for confirmation and comparison purposes.
* Exclusion criteria:

  * Pregnant women are excluded due to potential impacts on screening results and the necessity for special considerations during pregnancy.
  * Individuals with severe medical conditions or circumstances that may render histopathologic examination inappropriate or unsafe are excluded.
  * Patients with conditions that could interfere with the accuracy of screening results are excluded.
  * Follow-up screenings are not included in this study.

Conditions2

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