Abstract
Breast cancer is the most leading cancer disease which demolishes many women lives for the past few decades. It can be prevented and reduce the death rate by proper treatment and by diagnosis at early stage. Mammogram and MRI scanning are preferable for analyzing the internal functionality of breast in detail, and in our study, we used MRI images with various sequences like T1-W, T2-W and T1-contrast enhanced for validation. Our approach uses gaussian filters for preprocessing, probabilistic C means for tumor segmentation and convolutional neural network for tumor classification. Our approach yields 98% segmentation accuracy and 95% classification accuracy, and its performance is compared with the existing methods like FCM, FCM-VES, hybrid K-means for segmentation and SVM, KNN and HOS–SVM for classification. We apply various qualitative measures such as entropy, eccentricity, dice coefficient, MSE, PSNR, F-score, sensitivity, specificity and accuracy for validation. For our work, we utilized online dataset like BI RADS and clinical dataset and proved that our approach needs average of 6 s for processing the breast cancer images.
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Sumathi, R., Vasudevan, V. (2022). MRI Breast Tumor Extraction Using Possibilistic C Means and Classification Using Convolutional Neural Network. In: Sharma, D.K., Peng, SL., Sharma, R., Zaitsev, D.A. (eds) Micro-Electronics and Telecommunication Engineering . ICMETE 2021. Lecture Notes in Networks and Systems, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-16-8721-1_71
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