An investigation of Bayes algorithm and neural networks for identifying the breast cancer

Udayakumar, E.; Santhi, S.; Pandu, Vetrivelan

doi:10.4103/ijmpo.ijmpo_127_17

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…For instance, a study implemented in 2007 on a database of 1069 cases with coronary heart disease, indicated that ANN is the best identifier [21]. In 2017, several researchers included ANNs together with other classifiers in the same project [22], making use of the benefits provided by each of them in order to identify breast cancer.…”

Section: Related Workmentioning

confidence: 99%

Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

Albu¹,

Precup

Teban³

2019

FU Mech Eng

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The aim of this paper is to present several approaches by which technology can assist medical decision-making. This is an essential, but also a difficult activity, which implies a large number of medical and technical aspects. But, more important, it involves humans: on the one hand, the patient, who has a medical problem and who requires the best solution; on the other hand, the physician, who should be able to provide, in any circumstances, a decision or a prediction regarding the current and the future medical status of the patient. The technology, in general, and particularly the Artificial Intelligence (AI) tools could help both of them, and it is assisted by appropriate theory regarding modeling tools. One of the most powerful mechanisms that can be used in this field is the Artificial Neural Networks (ANNs). This paper presents some of the results obtained by the Process Control group of the Politehnica University Timisoara, Romania, in the field of ANNs applied to modeling, prediction and decision-making related to medical systems. An Iterative Learning Control-based approach to batch training a feedforward ANN architecture is given. The paper includes authors’ concerns in this domain and emphasizes that these intelligent models, even if they are artificial, are able to make decisions, being useful tools for prevention, early detection and personalized healthcare.

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Section: Related Workmentioning

confidence: 99%

Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

Albu¹,

Precup

Teban³

2019

FU Mech Eng

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“…The next process is to extract features from the targeted region [16]. After passing these features to a classifier, it determines whether these mammogram images are normal or abnormal [17]. Feature selection is a procedure that follows feature extraction and is frequently used in machine learning [18].…”

Section: Introductionmentioning

confidence: 99%

BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images

et al. 2023

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The early detection of breast cancer using mammogram images is critical for lowering women’s mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information. We created a new framework for diagnosing breast cancer using entropy-controlled deep learning and flower pollination optimization from the mammogram images. In the proposed framework, a filter fusion-based method for contrast enhancement is developed. The pre-trained ResNet-50 model is then improved and trained using transfer learning on both the original and enhanced datasets. Deep features are extracted and combined into a single vector in the following phase using a serial technique known as serial mid-value features. The top features are then classified using neural networks and machine learning classifiers in the following stage. To accomplish this, a technique for flower pollination optimization with entropy control has been developed. The exercise used three publicly available datasets: CBIS-DDSM, INbreast, and MIAS. On these selected datasets, the proposed framework achieved 93.8, 99.5, and 99.8% accuracy, respectively. Compared to the current methods, the increase in accuracy and decrease in computational time are explained.

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“…Research for the development of methods of detection and identification and classification of diseases based on medical images of the disease has been growing rapidly. Some examples of such research are the development of the Bayes algorithm and neural networks for breast cancer identification [10], the use of machine learning and radiomic features for automatic classification of benign and malignant breast cancer [11], standardization and normalization of data for the effectiveness of classifying spongy tissue textures [12]. There are various methods of detecting and classifying tumors [13], using a hybrid histogram based on the K-Means Clustering algorithm for leukemia [14], developing algorithms for the detection of region-of-interest (ROI) lung nodules [15], applying machine vision to detect lung nodules [15], and identification of kidney stones [16].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Histogram Features and Co-occurrence Matrix in Identification of Lung Cancer X-Ray Images with Naive-Bayes Method

Bustomi

Rusnandar

2022

J. Phys.: Conf. Ser.

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Many studies have been carried out on the identification of objects in the image. One feature often used in object identification is the histogram feature of digital images. The next development is the use of a digital image co-occurrence matrix as an identifier. This study aims to compare the use of two object identifiers in images that are not derived from visible light but are images derived from X-rays. In this study, the X-ray image is an X-ray image of lung cancer, and the object identification method used is the Naive Bayes method. The number of lung X-ray images used was 100 images. The master image is divided into 60 images for training and 40 images for testing. This study's results indicate that using a digital image co-occurrence matrix as an identifier gives better results than the histogram feature. This study concludes that the comparison of histogram features and co-occurrence matrix in X-ray images shows the same results as in visible light images.

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An investigation of Bayes algorithm and neural networks for identifying the breast cancer

Cited by 10 publications

References 8 publications

Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images

Comparison of Histogram Features and Co-occurrence Matrix in Identification of Lung Cancer X-Ray Images with Naive-Bayes Method

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