Enhancement of Deep Learning in Image Classification Performance Using VGG16 with Swish Activation Function for Breast Cancer Detection

Muduli, Debendra; Dash, Ratnakar; Majhi, Banshidhar

doi:10.1007/978-981-16-1086-8_17

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Two phases of processing make up a typical cloud operation. Attribute selection has been shown to boost the efficiency of machine learning techniques [57,58] by previous researchers. The initial step is to isolate the important features by employing the PCA technique, after which the unnecessary ones can be filtered out.…”

Section: Figure 1 Block Diagram Of the Experimental Model For The Ext...mentioning

confidence: 99%

A Diabetes Monitoring System and Health-Medical Service Composition Model in Cloud Environment

Sharma¹,

Zamani

Alsanad

et al. 2023

IEEE Access

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Diabetes is a common chronic illness or absence of sugar in the blood. The early detection of this disease decreases the serious risk factor. Nowadays, Machine Learning based cloud environment acts as a vital role in disease detection. The people who belong to the rural areas are not getting the proper health care treatments. So, this research work proposed an automated eHealth cloud system for detecting diabetes in the earlier stage to decrease the mortality rate and provides health treatment facilities to rural peoples. Extreme Learning Machine (ELM) is a type of Artificial Neural Network (ANN) that has a lot of potential for solving classification challenges. This research work is consisting of several activities like feature normalization, feature selection and classification. We have employed principal component analysis (PCA) for feature selection and extreme learning machine (ELM) for classification. Finally, a cloud computingbased environment with three numbers of virtual machines , is used for the detection of diabetes. The efficacy of the proposed model has been evaluated with the PIMA dataset in both standalone and cloud environments and achieved 90.57 % accuracy, 82.24 % sensitivity, 73.23 % specificity, and 75.03 % F-1 score with the virtual machine vCPU-16. The experimental results define the proposed model as superior to other state-of-art models with better classification accuracy and less number of features.

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Section: Figure 1 Block Diagram Of the Experimental Model For The Ext...mentioning

confidence: 99%

A Diabetes Monitoring System and Health-Medical Service Composition Model in Cloud Environment

Sharma¹,

Zamani

Alsanad

et al. 2023

IEEE Access

Self Cite

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“…This approach utilizes specific points of interest within a multi-dimensional feature space derived from fundus images, demonstrating robustness to changes in the area near the optic disc. Machine learning (ML) has been used to tackle various tasks involving the analysis of medical images, demonstrating impressive speed and efficiency in optimizing processes across a wide range of diseases, such as breast cancer diagnosis [5][6][7][8][9][10], diabetes detection [11,12], etc.…”

Section: Of 29mentioning

confidence: 99%

Discrete Ripplet-II Transform Feature Extraction and Metaheuristic-Optimized Feature Selection for Enhanced Glaucoma Detection in Fundus Images Using LS-SVM

Sharma,

Muduli,

Rath

et al. 2023

Preprint

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Recently, significant progress has been made in developing computer-aided diagnosis (CAD) systems for identifying glaucoma abnormalities using fundus images. Despite their drawbacks, methods for extracting features such as wavelets and their variations, along with classifier like support vector machines (SVM), are frequently employed in such systems. This paper introduces a practical and enhanced system for detecting glaucoma in fundus images. This system adresses the chanallages encountered by other existing models in recent litrature. Initially, we have employed contrast limited adaputive histogram equalization (CLAHE) to enhanced the visualization of input fundus inmages. Then, the discrete ripplet-II transform (DR2T) employing a degree of 2 for feature extraction. Subsequently, a golden jackal optimization algorithm (GJO) employed to select the optimal features to reduce the dimension of the extracted feature vector. During the classification stage the least square support vector machine (LS-SVM) with three kernels called as linear, polynomial and radial basis function(RBF), for classifying of fundus images as glaucoma or healthy. The proposed method is validated with the current state-of-the-art models on two standard datasets, namely, G1020 and ORIGA. The results obtained from our experimental result demonstrate that our best suggested approach DR2T+GJO+LS-SVM-RBF obtains better classification accuracy 93.38% and 97.31% for G1020 and ORIGA dataset with less number of features. It establishes a more concise network structure when contrasted with traditional classifiers.

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“…Chougrad et al [9] investigated the significance of transfer learning and tested various deep CNN models to find the optimum fine-tuning technique. With the Swish activation function, a modified VGG16 model was proposed in [10]. Authors have shown that the modified VGG16 model with the Swish activation function delivers better accuracy than Relu activation.…”

Section: Introductionmentioning

confidence: 99%

A Transfer Representation Learning Approach for Breast Cancer Diagnosis from Mammograms using EfficientNet Models

Oza¹,

Sharma²,

Patel³

2022

SCPE

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Breast cancer is a deadly disease that affects the lives of millions of women throughout the world. Over time, the number of cases of breast cancer has increased. Preventing this disease is difficult and remains unidentified, but the survival percentage can be improved if diagnosed early. The progress of computer-assisted diagnosis (CAD) of breast cancer has seen a lot of improvements thanks to advances in deep learning. With the notable advancement of deep neural networks, diagnostic capabilities are nearing a human expert's. In this paper, we used EfficientNet to classify mammograms. This model is introduced with the new concept of model scaling called compound scaling. Compound scaling is the strategy which scales the model by adding more layers to extend the receptive field along with more channels to catch the detailed features of larger input. We also compare the performance of various variants of EfficientNet over CBIS-DDSM mammogram datasets. We used the optimum fine-tuning procedure to represent the importance of transfer learning (TL) during training.

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Enhancement of Deep Learning in Image Classification Performance Using VGG16 with Swish Activation Function for Breast Cancer Detection

Cited by 8 publications

References 10 publications

A Diabetes Monitoring System and Health-Medical Service Composition Model in Cloud Environment

A Diabetes Monitoring System and Health-Medical Service Composition Model in Cloud Environment

Discrete Ripplet-II Transform Feature Extraction and Metaheuristic-Optimized Feature Selection for Enhanced Glaucoma Detection in Fundus Images Using LS-SVM

A Transfer Representation Learning Approach for Breast Cancer Diagnosis from Mammograms using EfficientNet Models

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