Deep Architecture based on DenseNet-121 Model for Weather Image Recognition

Albelwi, Saleh

doi:10.14569/ijacsa.2022.0131065

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…This can alleviate overfitting issues and enable training with smaller datasets [24]. In this study, the researcher utilized DenseNet121, a basic variant of the DenseNet architecture consisting of 121 layers, as depicted in Figure 5 [25]. In practice, DenseNet is commonly employed in tasks related to image recognition, such as image classification, object detection, and image segmentation.…”

Section: Densenetmentioning

confidence: 99%

DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character

Sudewo,

Biddinika,

Fadlil

2024

matrik

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This study introduced a specifically optimized DenseNet architecture for recognizing Javanese Hanacaraka characters, focusing on enhancing efficiency and accuracy. The research aimed to preserve and celebrate Java’s rich cultural heritage and historical significance through the development of precise character recognition technology. The method used advanced techniques within convolutional neural networks (CNN) to integrate feature extraction across densely connected layers efficiently. The result of this study was that the developed model achieved a training accuracy of 100% and a validation accuracy of approximately 99.50% after 30 training epochs. Furthermore, when tested on previously unseen datasets, the model exhibited exceptional accuracy, precision, recall, and F1-score, reaching 100%. These findings underscored the remarkable capability of DenseNet architecture in character recognition, even across novel datasets, suggesting significant potential for automating Javanese Hanacaraka text processing across various applications, ranging from text recognition to digital archiving. The conclusion drawn from this study suggests that optimizing DenseNet architecture can be a significant step in preserving and developing character recognition technology for Javanese

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Section: Densenetmentioning

confidence: 99%

DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character

Sudewo,

Biddinika,

Fadlil

2024

matrik

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“…Currently, several pre-trained DNN models exist that have the potential as baseline models to develop new models utilizing the TL approach, such as MobileNet [28], [37], MobileNetV2 [38], [39], EfficientNetB0, EfficientNetB1, EfficientNetB2 [29], [40] DenseNet121 [41], Xception [42], InceptionV3 [43], ResNet50 [44], and InceptionResNetV2 [45]. Each of these models offers its own set of advantages and limitations.…”

Section: Related Workmentioning

confidence: 99%

Baseline model for deep neural networks in resource-constrained environments: an empirical investigation

Careem,

Gapar Md Johar,

Khatibi

2024

IJCDS

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This paper presents an empirical study on advanced Deep Neural Network (DNN) models, with a focus on identifying potential baseline models for efficient deployment in resource-constrained environments (RCE). The systematic evaluation encompasses ten state-of-the-art pre-trained DNN models: ResNet50, InceptionResNetV2, InceptionV3, MobileNet, MobileNetV2, EfficientNetB0, EfficientNetB1, EfficientNetB2, DenseNet121, and Xception, within the context of an RCE setting. Evaluation criteria, such as parameters (indicating model complexity), storage space (reflecting storage requirements), CPU usage time (for real-time applications), and accuracy (reflecting prediction truth), are considered through systematic experimental procedures. The results highlight MobileNet's excellent trade-off between accuracy and resource requirements, especially in terms of CPU and storage consumption, in experimental scenarios where image predictions are performed on an RCE device. Utilizing the identified baseline model, a new model, GRM-MobileNet, was developed by implementing compound scaling and global average pooling techniques. GRM-MobileNet exhibits a substantial reduction of 23.81% in parameters compared to MobileNet, leading to a model size that is 23.88% smaller. Moreover, GRM-MobileNet demonstrates a significant improvement in accuracy, achieving a remarkable gain of 28.12% over MobileNet. Although the enhancement in inference time for GRM-MobileNet compared to MobileNet is modest at 1.66%, the overall improvements underscore the effectiveness of the employed strategies in enhancing the model's performance. A future study will examine other model optimization strategies, including factorization and pruning, which ultimately lead to faster inference without compromising accuracy, in an effort to improve the efficiency of the GRM-MobileNet model and its inference time.

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“…Additionally, TL improves generalisation, cuts down Figure 3: Architecture of Baseline EfficientNet-B0 [15] on overfitting, training time, and requires less labelled data. Recent years have seen extensive use of TL in computer vision [8][16] [17].…”

Section: E Transfer Learningmentioning

confidence: 99%

Deep Learni̇ng Archi̇techture for Classi̇fi̇cati̇on of Breast Cancer Cells i̇n Fluorescence Mi̇croscopy Images

Kakumani

2022

ijarcs

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Biological cell classification plays a significant role in the field of biomedical research. Cell classification is useful in different biomedical applications like identification of a normal and abnormal cell, cancer cell recognition, behovioural studies of cells to different drugs, etc. Automated cell classification techniquies would assist the radiologist for the disease diagnoses and to grasp the severity of the disease based on the intricate intracellular structures of the cells. In this work, a deep learning method based on EfficientNet is designed for automatic classification of human breast cancer cells from fluorescence microscopy images. More specifically, transfer learning is employed to take the advantage of the pretrained model and further improvising the performance of the network by fine tuning several of last layers for learning the specific classification task. The proposed deep learning method is evaluated on human breast cancer cells, which gave 98.15% accuracy, 98.33% precision, 98.15% recall and 98.14% F1 score. Comparitive analysis of the proposed method with the standard architectures is also performed to assert the efficacy of our model.

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Deep Architecture based on DenseNet-121 Model for Weather Image Recognition

Cited by 7 publications

References 25 publications

DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character

DenseNet Architecture for Efficient and Accurate Recognition of Javanese Script Hanacaraka Character

Baseline model for deep neural networks in resource-constrained environments: an empirical investigation

Deep Learni̇ng Archi̇techture for Classi̇fi̇cati̇on of Breast Cancer Cells i̇n Fluorescence Mi̇croscopy Images

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