Breast tumor detection using deep learning

Abstract

Finding tumor areas on breast ultrasonography pictures has long been a fascinating subject. The complex architecture of breast and the presence of noise in ultrasound images sometimes make it impossible for traditional handcrafted feature-based approaches to produce adequate results. The accuracy of finding objects has significantly improved with the recent developments in deep learning, particularly for generic detection of objects. Moreover, most models currently lack efficient optimization for the algorithm's structure, which incurs high processing costs during training and deployment. This paper offers a variety of image processing techniques for breast tumor classification. Finding breast tumor is the study's main objective. In order to accurately detect and classify images as benign or tumor, a number of methods and algorithms have been developed. In this particular experiment, ultrasound pictures of three different class kinds— malignant, benign, and normal—were combined with dl-based models. The prediction and detection of tumor pictures is done using five models: InceptionResNetV2, InceptionV3, VGG16, VGG19, and DenseNet169, to classify breast tumor stages. Finally, the results of the approach are assessed using two different measures of efficiency. Four possible outcomes—TP, TN, FP, and FN—are used in the first reliability set, a performance evaluation for the stages of tumor that follow. We next apply the above algorithms to analyze the accuracy of each type of breast tumor in mistake scenarios. The InceptionResNetV2 approach, which I suggest, allows for the autonomous recognition of breast tumors with an accuracy rate of 91.82%.