Framework for Detecting Breast Cancer Risk Presence Using Deep Learning

Abstract

Cancer is a complicated global health concern with a significant fatality rate. Breast cancer is among the leading causes of mortality each year. Advancements in prognoses have been progressively based primarily on the expression of genes, offering insight into robust and appropriate healthcare decisions, owing to the fast growth of advanced throughput sequencing techniques and the use of various deep learning approaches that have arisen in the past few years. Diagnostic-imaging disease indicators such as breast density and tissue texture are widely used by physicians and automated technology. The effective and specific identification of cancer risk presence can be used to inform tailored screening and preventive decisions. For several classifications and prediction applications, such as breast imaging, deep learning has increasingly emerged as an effective method. We present a deep learning model approach for predicting breast cancer risk primarily on this foundation. The proposed methodology is based on transfer learning using the InceptionResNetV2 deep learning model. Our experimental work on a breast cancer dataset demonstrates high model performance, with 91% accuracy. The proposed model includes risk markers that are used to improve breast cancer risk assessment scores and presents promising results compared to existing approaches. Deep learning models include risk markers that are used to improve accuracy scores. This article depicts breast cancer risk indicators, defines the proper usage, features, and limits of each risk forecasting model, and examines the increasing role of deep learning (DL) in risk detection. The proposed model could potentially be used to automate various types of medical imaging techniques.

Machine learning and new insights for breast cancer diagnosis

“…174 The current review extracted and organized the data in tabular form and summarized the application of CT, breast thermal imaging, PET and microwave imaging technology in the diagnosis of breast cancer (Table 4). 92,109,147,149,151,167,[174][175][176][177][178][179][180][181][182][183][184][185][186][187]…”

Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement

“…In this section, the approach's fundamental theory is outlined and explained. Inception-v3 [11,12] is one of transfer learning pretrained models, it is a succeeding of the original architecture for Inception-v1 [35] and Inception-v2 [36]. The Inception-v3 model is trained using the ImageNet datasets [37,38], which contain the information required for identifying one thousand classes.…”

“…In contrast to MIA, HEM is characterized by big spots on the retina with uneven edge widths of more than 125 micrometers. A hemorrhage can be either flame or blot, according to whether the spots are on the surface or deeper in the tissue [10,11].…”

“…Lesions caused by MIA and HEM tend to be red, while blemishes caused by the two forms of exudates tend to be bright. There are five distinct stages of DR that can be detected: Different degrees of DR are shown in Figure 1: none, mild, moderate, severe, and proliferative DR [11]. .…”

Enhancement of Diabetic Retinopathy Prognostication Utilizing Deep Learning, CLAHE, and ESRGAN

“…Extraction of features using machine learning (ML) approaches was the foundation of the vast bulk of DR research prior to the problem of manual feature extraction prompted researchers to shift their focus to deep learning (DL) [11].…”