Breast Cancer Detection using Crow Search Optimization based Intuitionistic Fuzzy Clustering with Neighborhood Attraction

Abstract

Objective: Generally, medical images contain lots of noise that may lead to uncertainty in diagnosing the abnormalities. Computer aided diagnosis systems offer a support to the radiologists in identifying the disease affected area. In mammographic images, some normal tissues may appear to be similar to masses and it is tedious to differentiate them. Therefore, this paper presents a novel framework for the detection of mammographic masses that leads to early diagnosis of breast cancer. Methods: This work proposes a Crow search optimization based Intuitionistic fuzzy clustering approach with neighborhood attraction (CrSA-IFCM-NA) for identifying the region of interest. First order moments were extracted from preprocessed images. These features were given as input to the Intuitionistic fuzzy clustering algorithm. Instead of randomly selecting the initial centroids, crow search optimization technique is applied to choose the best initial centroid and the masses are separated. Experiments are conducted over the images taken from the Mammographic Image Analysis Society (mini-MIAS) database. Results: CrSA-IFCM-NA effectively separated the masses from mammogram images and proved to have good results in terms of cluster validity indices indicating the clear segmentation of the regions. Conclusion: The experimental results show that the accuracy of the proposed method proves to be encouraging for detection of masses. Thus, it provides a better assistance to the radiologists in diagnosing breast cancer at an early stage.

Role of microRNAs in triple‑negative breast cancer and new therapeutic concepts (Review)

“…Full-field mammography has the capability of inputting raw images and outputting processed images, where AI analyzes the images and analyzes the breast mass, mass segmentation, tissue density and risk assessment. The breast mass is the most common manifestation of breast cancer assessment and therefore CAD becomes one of the most important steps ( 97 ). Another method to assess the risk of breast cancer is calcification.…”

Scale Invariant Feature Transform with Crow Optimization for Breast Cancer Detection

“…In the RBM, the connection between the visible layer and hidden layers is restricted. To transmit the input data to the hidden layer, the RBM layer communicates with previous and subsequent layers [34].To transform input data from visible to hidden layers, use a sigmoid function with the RBM learning rule. The framework of DBN with RBM is shown in Fig.…”

Artificial Intelligence in Medical Imaging of the Breast

“…Mammography can detect breast masses that cannot be palpated by doctors and can reliably identify benign lesions and malignant tumors of the breast. Mammograms are currently acquired with full-field digital mammography (DM) systems and are provided in both forprocessing (the raw imaging data) and for-presentation (a postprocessed version of the raw data) image formats (23,24). To date, AI has been used to analyze mammography images in most studies mainly for the detection and classification of breast mass and microcalcifications, breast mass segmentation, breast density assessment, breast cancer risk assessment and image quality improvement.…”

“…Therefore, mass detection is an essential step in CAD. Some studies proposed a Crow search optimization based intuitionistic fuzzy clustering approach with neighborhood attraction (CrSA-IFCM-NA), and it has been proven that CrSA-IFCM-NA effectively separated the masses from mammogram images and had good results in terms of cluster validity indices, indicating the clear segmentation of the regions (24). Others developed a complete integrated CAD system, which included a regional DL approach You-Only-Look-Once (YOLO) and a new deep network model full resolution convolutional network (FrCN) and a deep CNN, to detect, segment, and classify masses in mammograms and used the INbreast dataset to verify that quality detection accuracy reached 98.96%, effectively assisting radiologists make an accurate diagnosis (16,25,26).…”