Stacking approach for accurate Invasive Ductal Carcinoma classification

The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems.

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“… 2021 ; Haq et al. 2022 ). The CNN model extracts deep features from image data, and these features were crucial in final image classification.…”

Section: Discussionmentioning

confidence: 99%

“… 2021 ; Haq et al. 2022 ) were used for the evaluation of the proposed model. These metrics are mathematically expressed as follows in Eqs.…”

Section: Methodsmentioning

confidence: 99%

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

Haq

Kumar

et al. 2022

J Ambient Intell Human Comput

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“…The CNNs technique will retrieve increasingly closely related characteristics from the data for accurate picture classification [10]. In IoT medical field, data augmentation and transfer learning techniques could also increase the predictive potential of deep learning techniques to precisely diagnose brain cancer and classify brain tumours [11].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor classification using BrainNet: A Deep Learning Approach

Wagh

Bhosale

Singh

et al. 2023

Preprint

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Cancers of any type are life-threatening diseases and totally hamper normal body functions. One of the leading causes of death in both adults and children is malignant brain tumors. Asa result, detecting brain tumors in their early stages is critical for accurate diagnosis. Magnetic resonance imaging (MRI) is commonly used to diagnose brain tumors. Brain tumors are complex due to extremely erratic shapes and locations and are challenging to fully understand the nature of the tumor. For MRI analysis, a skilled neurosurgeon is also required. Because there are frequently insufficient highly trained medical experts and a general lack of knowledge about tumors in developing countries, obtaining results from MRIs can be extremely difficult and time-consuming. To overcome this drawback of this, proposed a novel Convolution Neural Network(CNN)with BrainNet for the multiclass classification of tumors. The proposed BrianNet performs better than transfer learning models VGG13, VGG16, VGG19, Squeeznet, and InceptionResV2 which are pre-trained on the Imagenet dataset. The proposed CNN (BrainNet) architecture achieved a training accuracy of 99.96% and test accuracy of 97.71% and an average precision of 94.75%. Medical professionals can classify tumours with the aid of the proposed work.

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“…The CNNs model can extract more related features from data for accurate image classification 2 , 5 , 6 . Furthermore, data augmentation and transfer learning techniques can also improve the predictive capability of deep learning models to effective classify the brain tumors and diagnosis brain cancer in IoT healthcare industries 6 , 7 .…”

Section: Introductionmentioning

confidence: 99%

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment

Haq

Khan

et al. 2022

Sci Rep

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The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the accurate detection of brain cancer. However, due to the inaccuracy of artificial diagnostic systems, medical professionals are not effectively incorporating them into the diagnosis process of Brain Cancer. In this research study, we proposed a robust brain tumor classification method using Deep Learning (DL) techniques to address the lack of accuracy issue in existing artificial diagnosis systems. In the design of the proposed approach, an improved convolution neural network (CNN) is used to classify brain tumors employing brain magnetic resonance (MR) image data. The model classification performance has improved by incorporating data augmentation and transfer learning methods. The results confirmed that the model obtained high accuracy compared to the baseline models. Based on high predictive results we suggest the proposed model for brain cancer diagnosis in IoT-healthcare systems.

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Stacking approach for accurate Invasive Ductal Carcinoma classification

Cited by 18 publications

References 16 publications

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

Brain Tumor classification using BrainNet: A Deep Learning Approach

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment

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