An Intelligent Internet of Medical Things with Deep Learning Based Automated Breast Cancer Detection and Classification Model

Mathapati, Mahantesh; Chidambaranathan, S.; Nasir, Abdul Wahid; Vimalarani, G.; Rani, S. Sheeba; Gopalakrishnan, T.

doi:10.1007/978-3-030-55833-8_11

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…This is comparable with our result. Results achieved with homogeneity, energy, HOG features and DNN, NB, NN, SVM classification are also reported in [11] with accuracies between 42.6% and 59.6%. These methods are outperformed by the proposed extended method.…”

Section: Experiments and Resultsmentioning

confidence: 91%

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Extended Mammogram Classification From Textural Features

Bajcsi¹,

Chira²,

Andreica³

2023

Studia UBB Informatica

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"The efficient analysis of digital mammograms has an important role in the early detection of breast cancer and can lead to a higher percentage of recovery. This paper presents an extended computer-aided diagnosis system for the classification of mammograms into three classes (normal, benign and malignant). The performance of the system is evaluated for two different mammogram databases (MIAS and DDSM) in order to assess its robustness. We discuss the changes required in the system, particularly at the level of the image preprocessing and feature extraction. Computational experiments are performed based on different methods for feature extraction, selection and classification. The results indicate an accuracy of 66.95% for the MIAS dataset and 54.1% for DDSM obtained using genetic algorithm based feature selection and Random Forest classification. Keywords: Breast cancer detection, Mammogram classification, GLRLM, Feature selection, Random Forests, MIAS, DDSM. "

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Section: Experiments and Resultsmentioning

confidence: 91%

“…In [11] presents a comparison of different combinations of feature selection and classification (evaluated on MIAS). The best result reported was 70.53% using Local Binary Pattern and Deep Neural Network.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Extended Mammogram Classification From Textural Features

Bajcsi¹,

Chira²,

Andreica³

2023

Studia UBB Informatica

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“…The overall 322 mammogram images with 104 glandular, 106 fatties and 112 dense cases were obtained from the Mammogram Image Analyses Society dataset. Mathapati et al [12] presented a smart IoMT-based BC diagnosis and detection using a DL method. IoMT-based image acquisition procedure occurs for gathering digital mammogram images.…”

Section: Prior Bc Diagnosis Models In a Smart Healthcare Environmentmentioning

confidence: 99%

Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

Mansour¹,

Althobaiti²

2022

Computers, Materials &Amp; Continua

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Recently, the Internet of Medical Things (IoMT) has become a research hotspot due to its various applicability in medical field. However, the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment, generating a massive quantity of healthcare data. In such cases, cognitive computing can be employed that uses many intelligent technologies-machine learning (ML), deep learning (DL), artificial intelligence (AI), natural language processing (NLP) and others-to comprehend data expansively. Furthermore, breast cancer (BC) has been found to be a major cause of mortality among ladies globally. Earlier detection and classification of BC using digital mammograms can decrease the mortality rate. This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm (DL-MOMFO) for BC diagnosis and classification in the IoMT environment. The goal of this paper is to integrate deep learning (DL) and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC. The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter (AWMF)-based noise removal and contrast-limited adaptive histogram equalisation (CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms. In addition, a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms. Moreover, a SqueezeNet-based feature extraction and a fuzzy support vector machine (FSVM) classifier were used in the presented technique. To enhance the diagnostic performance of the presented method, the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques. The DL-MOMFO technique was tested on the MIAS database, and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.

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Radon Transformation-Based Mammogram Image Classification

Sharma,

Purwar

2023

Lecture Notes in Electrical Engineering

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An Intelligent Internet of Medical Things with Deep Learning Based Automated Breast Cancer Detection and Classification Model

Cited by 6 publications

References 8 publications

Extended Mammogram Classification From Textural Features

Extended Mammogram Classification From Textural Features

Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

Radon Transformation-Based Mammogram Image Classification

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