International Journal For Multidisciplinary Research

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Unveiling the Secrets of the Mammogram: A Statistical Journey through Machine Learning Approach

Author(s) A.Jagadish Kumar, K.Karteeka Pavan, A.V.Dattatreya Rao
Country India
Abstract Mammograms, once mere X-ray images, are now powerful allies in the fight against breast cancer. Armed with sophisticated statistical image classification techniques, these images reveal hidden clues that can lead to early detection. Beneath the surface of a mammogram lies a complex tapestry of patterns, each one a potential marker of malignancy. By decoding these intricate patterns, algorithms can identify abnormalities that might otherwise go unnoticed. The Mammographic Image Analysis Society (MIAS) has been at the forefront of this revolution, creating a vast database of digital mammograms. From this treasure trove, we've selected 322 images to put our statistical prowess to the test. Using a powerful machine learning tool known as Support Vector Machine (SVM), we've classified these images into seven categories:

• Calcifications (CALC): Tiny, hard deposits.
• Circumscribed (CIRC) masses: Well-defined lumps.
• Speculated (SPIC) masses: Lumps with spiky edges.
• Ill-defined (MISC) masses: Irregular, indistinct lumps.
• Architectural distortions (ARCH): Changes in the breast's structure.
• Asymmetry(s) (ASYM): Differences between the two breasts.
• Normal (NORM): Healthy breast tissue.

Our goal is to shed light on the intricate process of mammogram classification and share the insights gained from our analysis presented in the user-friendly language and the analysis is carried out using MATLAB.
Keywords Mammogram, GLCM, GLAM
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-27
Cite This Unveiling the Secrets of the Mammogram: A Statistical Journey through Machine Learning Approach - A.Jagadish Kumar, K.Karteeka Pavan, A.V.Dattatreya Rao - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.31697
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.31697
Short DOI https://doi.org/g8r8f3
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