Adaptive Resource Allocation Neural Network-Based Mammogram Image Segmentation and Classification

Abstract

Image processing innovations assume a significant part in diagnosing and distinguishing diseases and monitoring these diseases’ quality. In Medical Images, detection of breast cancer in its earlier stage is most important in this field. Because of the low contrast and uncertain design of the tumor cells in breast images, it is still challenging to classify breast tumors only by visual testing by the radiologists. Hence, improvement of computer-supported strategies has been introduced for breast cancer identification. This work presents an efficient computer-assisted method for breast cancer classification of digital mammograms using Adaptive Resource Allocation Network (ARAN). At first, breast cancer images were taken as input, preprocessing step is utilized to eliminate the noise and unimportant data from the image utilizing a Butterworth filter. Adaptive histogram equalization is utilized to improve the contrast of the image. Multimodal clustering segmentation has been applied, and Tetrolet transformation based feature extraction is applied at various levels, based on this, data classification is implemented. For exact classification, ARAN is utilized to predict if the patient is influenced by breast cancer. Compared with other current research techniques, the proposed strategy predicts the results efficiently. The overall accuracy of ARAN-based mammogram classification is 93.3%.