Performance Evaluation of Hybrid Machine Learning Algorithms for Medical Image Classification

Renukadevi, N. T.

doi:10.1007/978-3-030-75657-4_12

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…The results show that each employed algorithm detected breast cancer with a relatively high accuracy and well-defined efficiency. The SVM was the most efficient classifier in the diagnosis of breast cancer based on the outcomes, showcasing an ultimate accuracy of 96.84%, the greatest specificity of 97.36%, and the greatest sensitivity of 95.85%; this high performance of the SVM was in accordance with that reported in medical-related studies [31]. However, the difference in the performance of the algorithms should be noted by taking the SVM as a reference, as shown in Table 4.…”

Section: Discussionsupporting

confidence: 83%

“…On the other hand, ML aided in the automatic classification of breast cancer and produced promising results when applied to a data set that was labeled as malignant and benign. This was in accordance with the general concept triggering the use of ML [4,31] as, usually, when doctors want to specify the type of tumor-whether it be malignant or benign-they use their own experience acquired throughout their career and study [3]; that is, they rely on their personal logic, mental effort, and personal intelligence to determine the diagnostic result, which is provided automatically to help them in the decision.…”

Section: Discussionsupporting

confidence: 64%

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Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models

Zaylaa,

Kourtian

2024

Sensors

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Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionsupporting

confidence: 64%

Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models

Zaylaa,

Kourtian

2024

Sensors

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Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images

Manjunath

Ghanshala

Kwadiki

2023

Multimed Tools Appl

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To diagnose the liver diseases computed tomography images are used. Most of the time even experienced radiologists find it very tough to note the type, size, and severity of the tumor from computed tomography images due to various complexities involved around the liver. In recent years it is very much essential to develop a computer-assisted imaging technique to diagnose liver disease in turn which improves the diagnosis of a doctor. This paper explains a novel deep learning model for detecting a liver disease tumor and its classification. Tumor from computed tomography images has been classified between Metastasis and Cholangiocarcinoma. We demonstrate that our model predominantly performs very well concerning the accuracy, dice similarity coefficient, and specificity parameters compared to well-known existing algorithms, and adapts very well for different datasets. A dice similarity coefficient value of 98.59% indicates the supremacy of the model.

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Performance Evaluation of Genetic Algorithm for Medical Image Retrieval

Renukadevi,

Saraswathi,

Nandhinidevi

2024

Bio-Inspired Intelligence for Smart Decision-Making

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Medical imaging is the procedure of generating diagnostic and treatment images of the human body, utilizing techniques such as traditional x-rays, magnetic resonance imaging, and positron emission tomography. The sheer volume of digital medical images stored in repositories poses a significant challenge for efficient access. To address this, content-based image retrieval is employed on visually analyzing the contents of a query image to retrieve relevant images. CBIR involves two key processes: feature extraction and feature matching. The primary hurdle in CBIR lies in developing flexible methodologies capable of processing diverse images with varying characteristics like color, shape, etc. This chapter concentrates on the retrieval of medical imagery as of various data sources and assesses the performance of machine learning classifiers, including support vector machine methodologies, with the aim of enhancing classification accuracy. In order to enhance the performance of classifiers, genetic algorithm is used as the tool for optimizing and for decision making for quick retrieval.

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Performance Evaluation of Hybrid Machine Learning Algorithms for Medical Image Classification

Cited by 4 publications

References 28 publications

Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models

Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models

Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images

Performance Evaluation of Genetic Algorithm for Medical Image Retrieval

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