Early predictive model for breast cancer classification using blended ensemble learning

Mahesh, T R; Kumar, V. Vinoth; Vivek, V.; Raghunath, K. M. Karthick; Madhuri, G Sindhu

doi:10.1007/s13198-022-01696-0

Cited by 35 publications

(12 citation statements)

References 19 publications

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“…Hence, the death rate of women due to breast cancer is 1 in 37 subjects, which is around 2.7%. Therefore, the proper treatment and early diagnosis of breast cancer are essential for decreasing the death rates and preventing the disease progression [4]- [6]. In recent decades, magnetic resonance imaging (MRI) images are highly utilized for diagnosing breast cancer to decrease unnecessary biopsies [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

Kumar Mamdy,

Petli

2024

IJECE

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Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.

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Section: Introductionmentioning

confidence: 99%

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

Kumar Mamdy,

Petli

2024

IJECE

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“…The basic principle of the smart healthcare ecosystem is to utilize smart devices, sensors, and IoT to provide improved healthcare services to smart city residents at any location and time at a low cost [7] [8]. Because the physician-to-patient ratio in developing countries is so standard, smart healthcare can help monitor patient health from afar using body sensors that transfer data to the cloud.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Hybrid Network Architecture to Diagnose IoT Sensor Signal in Healthcare System

Satheesh

Koti

2023

JMC

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IoT is a fascinating technology in today's IT world, in which items may transmit data and interact through intranet or internet networks. TheInternet of Things (IoT) has shown a lot of promise in connecting various medical equipment, sensors, and healthcare specialists to provide high-quality medical services from afar. As a result, patient safety has improved, healthcare expenses have fallen, healthcare service accessibility has increased, and operational efficiency has increased in the healthcare industry. Healthcare IoT signal analysis is now widely employed in clinics as a critical diagnostic tool for diagnosing health issues. In the medical domain, automated identification and classification technologies help clinicians make more accurate and timely diagnoses. In this paper, we have proposed a Deep Learning-Based hybrid network architecture (CNN-R-LSTM (DCRL)) that combines the characteristics of a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) based long-short-term memory (LSTM) to diagnose IoT sensor signals and classify them into three categories: healthy, patient, and serious illness. Deep CNN-R-LSTM Algorithm is used for classify the IoT healthcare data support via a dedicated neural networking model. For our study, we have used the MIT-BIH dataset, the Pima Indians Diabetes dataset, the BP dataset, and the Cleveland Cardiology datasets. The experimental results revealed great classification performance in accuracy, specificity, and sensitivity, with 99.02 percent, 99.47 percent, and 99.56 percent, respectively. Our proposed DCLR model is based on healthcare IoT Centre inputs enhanced with the centenary, which may aid clinicians in effectively recognizing the health condition.

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“…Globally, countless individuals are grappling with neurodegenerative disorders, notably Parkinson's disease (PD) and Alzheimer's disease (AD). AD is marked by an insidious cognitive decline, encompassing learning difficulties [1], compromised cognition, memory deficits, and impaired executive thinking [2]. Furthermore, non-motor symptoms such as disorientation, anxiety, and parasympathetic dysfunctions may also manifest [3].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Spatial Feature-CNN Employing Grad-CAM for Enhanced Segmentation and Classification in Alzheimer's and Parkinson's Disease Diagnosis via MRI

Asokan,

Seshadri

2023

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The precise diagnosis of neurodegenerative disorders, notably Parkinson's disease (PD) and Alzheimer's disease (AD), presents a formidable challenge, often necessitating several years for definitive determination. Given the increasing prevalence of PD and AD in aging populations of affluent nations, there is an urgent need for advanced technology and more precise diagnostic methodologies, particularly for early disease stages. In recent years, segmentation has seen a surge in application for processing Magnetic Resonance (MR) brain data, emerging as a valuable and indispensable tool. To capture comprehensive images of the brain for the diagnosis and classification of these neurodegenerative disorders, Magnetic Resonance Imaging (MRI) is employed. However, early detection and classification of PD and AD utilizing MRI datasets pose significant complexities. Owing to the inherent subjectivity of human observation, automated segmentation of MRI images has become a crucial asset for healthcare professionals. The primary focus of this study is to devise an effective image segmentation approach and classification techniques for the detection and categorization of AD and PD. Initially, Hierarchical Spatial Feature-CNN is employed to segment abnormal traces of PD and AD in MRIs. Subsequently, the Gradient-weighted Class Activation Mapping (Grad-CAM) method is used for disease classification. In Grad-CAM, each neuron is assigned prioritization weights based on their contribution to the classification of interest, using gradient information flowing into the final convolutional layer of the Convolutional Neural Network (CNN). Thus, the combination of Grad-CAM with CNN is applied to address the classification challenges inherent in PD and AD. Extensive experiments were conducted on the proposed model, resulting in a classification accuracy exceeding 98.17%. In addition, the proposed integration of Grad-CAM with CNN outperformed existing state-of-the-art approaches across all performance measures. This study underscores the potential of the proposed model in enhancing the diagnostic process for neurodegenerative disorders, offering promise for more efficient and accurate detection and classification of PD and AD.

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Early predictive model for breast cancer classification using blended ensemble learning

Cited by 35 publications

References 19 publications

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

Deep Learning Based Hybrid Network Architecture to Diagnose IoT Sensor Signal in Healthcare System

Hierarchical Spatial Feature-CNN Employing Grad-CAM for Enhanced Segmentation and Classification in Alzheimer's and Parkinson's Disease Diagnosis via MRI

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