Hybrid Statistical and Texture Features with DenseNet 121 for Breast Cancer Classification

Abstract

Cancers are aggressive which is coupled with higher mortality rates and remain a major life-threatening factor in humans. Thus, early detection of cancer among patients is important as it showed a great survival chance. Therefore, early detection provides the patients with greater survival chances. The diagnosis performed for the mammography images are quite expensive and also radiations produced during the process were harmful to the patients. Thermography is a cost-effective and invasive method compared to mammography images and thus has reached its popularity. The present research is aimed to create the Machine Learning (ML) models using Convolutional Neural Networks (CNN) approaches which were created based on the machine learning models. The present research work utilized the DMR-IR dataset for the results evaluation and performance evaluation of the model which has been verified with datasets. The feature extraction process utilizes a machine learning algorithm to overcome the problems. The developed models were engaged in sophisticated ways to extract the features to improve the classification of the model. The Hybrid statistical and texture feature extraction technique extracts the features better in turn improved the model training. The results showed that the proposed hybrid feature extraction with the Dense Net 121 model obtained better

A Systematic Literature Review on the Methods of Breast Cancer Classification

“…WHO notes, deaths caused by cancer will reach 10 million cases in 2021. This number has increased by 0.4% compared to the previous year of 9.96 million cases [1]. Of many cancers, breast cancer is a cancer with the most cases.…”

Integrating Deep and Handcrafted Features forEnhanced Decision-Making Assistance in BreastCancer Diagnosis on Ultrasound Images

“…Finally, Principal Component Analysis (PCA) has been employed to reduce computational cost by decreasing the dimensionality of the feature space. Hiremath et al [28] have developed an approach to breast cancer diagnosis using a hybrid feature extraction model based on DenseNet 121 architecture. The hybrid feature extraction technique has combined statistical and texture feature extraction methods to enhance the learning rate of the DenseNet 121 model.…”