Alzheimer Disease Detection Empowered with Transfer Learning

Ghazal, Taher M.; Abbas, Sagheer; Munir, Sundus; Ahmad, Munir; Issa, Ghassan F.; Zahra, Syeda Binish; Khan, Muhammad Adnan; Hasan, Mohammad Kamrul

doi:10.32604/cmc.2022.020866

Cited by 156 publications

(39 citation statements)

References 19 publications

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“…The use of DL techniques for breast ultrasound lesion identification is proposed in this study ( 12 ), and three alternative methods are investigated: patch-based Le Net, transfer learning ( 13 ), and U-Net approach with the AlexNet model. Two conventional ultrasound picture datasets were obtained, and two separate ultrasound devices are compared and contrasted in this study.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

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Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

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Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

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“…The resolution of each figure was at least 224 × 224 pixels and a minimum of 46 figures were obtained from different views for each species. The dataset was later randomly divided into two sets based on [30,[60][61][62][63]: a training set of 80% and a validation set of 20%. The samples used are indicated in the following Fig.…”

Section: Results and Analysis 41 Experimental Settingsmentioning

confidence: 99%

An Improved Transfer-Learning for Image-Based Species Classification of Protected Indonesians Birds

Yang¹,

Harjoseputro²,

Hu³

et al. 2022

Computers, Materials &Amp; Continua

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This research proposed an improved transfer-learning bird classification framework to achieve a more precise classification of Protected Indonesia Birds (PIB) which have been identified as the endangered bird species. The framework takes advantage of using the proposed sequence of Batch Normalization Dropout Fully-Connected (BNDFC) layers to enhance the baseline model of transfer learning. The main contribution of this work is the proposed sequence of BNDFC that can be applied to any Convolutional Neural Network (CNN) based model to improve the classification accuracy, especially for image-based species classification problems. The experiment results show that the proposed sequence of BNDFC layers outperform other combination of BNDFC. The addition of BNDFC can improve the model's performance across ten different CNN-based models. On average, BNDFC can improve by approximately 19.88% in Accuracy, 24.43% in F-measure, 17.93% in G-mean, 23.41% in Sensitivity, and 18.76% in Precision. Moreover, applying fine-tuning (FT) is able to enhance the accuracy by 0.85% with a smaller validation loss of 18.33% improvement. In addition, MobileNetV2 was observed to be the best baseline model with the lightest size of 35.9 MB and the highest accuracy of 88.07% in the validation set.

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“…GA provides GP with the ability to choose features, but it is considerably broader. GP is beneficial for evaluating the efficiency of features and determining whether characteristics can survive the evolutionary process [17][18][19][20][21].…”

Section: Genetic Programmingmentioning

confidence: 99%

Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification

Bhardwaj

Sakalle

et al. 2022

Computational Intelligence and Neuroscience

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Breast cancer (BC) is the second leading cause of death in developed and developing nations, accounting for 8% of deaths after lung cancer. Gene mutation, constant pain, size fluctuations, colour (roughness), and breast skin texture are all characteristics of BC. The University of Wisconsin Hospital donated the WDBC dataset, which was created via fine-needle aspiration (biopsies) of the breast. We have implemented multilayer perceptron (MLP), K-nearest neighbor (KNN), genetic programming (GP), and random forest (RF) on the WBCD dataset to classify the benign and malignant patients. The results show that RF has a classification accuracy of 96.24%, which outperforms all the other classifiers.

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Alzheimer Disease Detection Empowered with Transfer Learning

Cited by 156 publications

References 19 publications

Breast Cancer Detection and Classification Empowered With Transfer Learning

Breast Cancer Detection and Classification Empowered With Transfer Learning

An Improved Transfer-Learning for Image-Based Species Classification of Protected Indonesians Birds

Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification

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