Detecting Breast Cancer in X-RAY Images Using Image Segmentation Algorithm and Neural Networks

Breast cancer presents a global health challenge that is endangering the lives of women all over the world. Because of this, many researches are attempting to provide an early detection technique to lessen the danger that breast cancer may cause; with a potential impact of saving 30% of the afflicted populace. Mammography, employing X-ray irradiation, serves as a quintessential modality for the identification of breast anomalies including neoplastic obstructions, discomfort, and nipple exudations. Concurrently, deep learning, a subset of artificial intelligence, has garnered momentum in the realm of breast carcinoma diagnostics. This paradigm facilitates the automated detection and categorization of neoplastic formations within mammographic imagery, as well as other radiological techniques, by learning to discern patterns autonomously without explicit algorithmic instructions. Deep learning algorithms are capable of learning to detect patterns in medical images without being explicitly programmed. This technology is being used to detect breast cancer earlier and more accurately than ever before. With the help of deep learning, radiologists can identify suspicious lesions, classify them as benign or malignant, and even predict the risk of recurrence of a malignant tumor. Furthermore, it enables the visualization of tumors that might elude unaided ocular inspection. Several types of neural network architectures, including but not limited to conventional and artificial neural networks, have been deployed in various studies for neoplasm detection, this task needs a preprocessing task depending on image processing like filtering, images enhancement, and gray levels detection to isolate and detect even the smallest areas in X-RAY images. This search uses image processing and computer vision approach to detect and recognize tumor areas in an X-RAY with the aid of neural networks to classify the danger level of the disease automatically. © 2023 Slovene Society Informatika. All rights reserved.