An Examination System to Classify the Breast Thermal Images into Early/Acute DCIS Class

Dey, Nilanjan; Rajinikanth, V.; Hassanien, Aboul Ella

doi:10.1007/978-981-15-7561-7_17

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…Some research works have used smallsized datasets [31], while some have not focused on segmenting the images [32]. And some of the works have focused on manually extracting breast areas in the mammogram images [33,34]. Due to these processes, the time and computational complexities have increased.…”

Section: Literature Surveymentioning

confidence: 99%

DLF: A Deep Learning Framework Using Convolution Neural Network Algorithm for Breast Cancer Detection and Classification

Govindarajan,

Narayanasamy

2024

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Section: Literature Surveymentioning

confidence: 99%

DLF: A Deep Learning Framework Using Convolution Neural Network Algorithm for Breast Cancer Detection and Classification

Govindarajan,

Narayanasamy

2024

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“…To improve image quality, the Firefly algorithm was used. [22] SVM-coarse Gaussian and SVM-cubic thermal pictures of the breast…”

Section: Breast Thermal Imagesmentioning

confidence: 99%

Breast Cancer Detection by Extracting and Selecting Features Using Machine Learning

Tambat,

Bhura,

Amdani

et al. 2023

IJRITCC

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The cancer of the breast is a significant cause of female death worldwide, but especially in developing countries. For better results and higher survival rates, early diagnosis and screening are crucial. Machine learning (ML) methods can aid in the initialdiscovery and diagnosis of breast cancer by choosing the most informative elements from medical data and eliminating irrelevant ones. The approach of feature extraction involves taking unstructured data and extracting a representative set of characteristics that may be used to classify or forecast data. The aim is to decrease the dimensionality of the feature space while upholding or even refining the accuracy of the ML model. An artificial intelligence model is developed on the given features to categorize mammography images into benign and malignant groups. Different supervised learning techniques, including support vector machines, random forests, and artificial neural networks, are employed and contrasted in order to select the best-performing model. This research offers a comprehensive framework for utilizing machine learning methods to detect breast cancer. The technique demonstrates how it might assist radiologists in the early detection of breast cancer by effectively extracting and selecting critical characteristics that could improve patient outcomes and potentially save lives.

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“…The proposed system extracted the breast area manual and results can't be generalized due to limitations of the dataset. Dey et al [24] extract 112 features by using texture features and entropy features. They used DT, KNN, SVM1 and SVM-RBF (SVM2) as classifiers.…”

Section: Plos Onementioning

confidence: 99%

“…2. some related work did not consider segmentation of the breast area before classification such as in [30] or extract the breast area manually such as in [23,24].…”

Section: Plos Onementioning

confidence: 99%

Deep learning model for fully automated breast cancer detection system from thermograms

et al. 2022

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Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

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An Examination System to Classify the Breast Thermal Images into Early/Acute DCIS Class

Cited by 8 publications

References 35 publications

DLF: A Deep Learning Framework Using Convolution Neural Network Algorithm for Breast Cancer Detection and Classification

DLF: A Deep Learning Framework Using Convolution Neural Network Algorithm for Breast Cancer Detection and Classification

Breast Cancer Detection by Extracting and Selecting Features Using Machine Learning

Deep learning model for fully automated breast cancer detection system from thermograms

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