Review of AlexNet for Medical Image Classification

Tang, Wenhao; Sun, Junding; Wang, Shuihua; Zhang, Yudong

doi:10.4108/eetel.4389

Cited by 3 publications

(1 citation statement)

References 86 publications

(100 reference statements)

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“…First, we compare the per-formance of the proposed method with DenseNet having no attention mechanism. Then, we compare it with AlexNet [9] followed by SqueezeNet [10]. ATT-DenseNet outperforms these two baseline deep learning architectures by achieving increased accuracy and an increased F1-score.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based DenseNet for Lung Cancer Classification Using CT Scan and Histopathological Images

Uddin

2024

Designs

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Lung cancer is identified by the uncontrolled proliferation of cells in lung tissues. The timely detection of malignant cells in the lungs, crucial for processes such as oxygen provision and carbon dioxide elimination in the human body, is imperative. The application of deep learning for discerning lymph node involvement in CT scans and histopathological images has garnered widespread attention due to its potential impact on patient diagnosis and treatment. This paper suggests employing DenseNet for lung cancer detection, leveraging its ability to transmit learned features backward through each layer continuously. This characteristic not only reduces model parameters but also enhances the learning of local features, facilitating a better comprehension of the structural complexity and uneven distribution in CT scans and histopathological cancer images. Furthermore, DenseNet accompanied by an attention mechanism (ATT-DenseNet) allows the model to focus on specific parts of an image, giving more weight to relevant regions. Compared to existing algorithms, the ATT-DenseNet demonstrates a remarkable enhancement in accuracy, precision, recall, and the F1-Score. It achieves an average improvement of 20% in accuracy, 19.66% in precision, 24.33% in recall, and 22.33% in the F1-Score across these metrics. The motivation behind the research is to leverage deep learning technologies to enhance the precision and reliability of lung cancer diagnostics, thus addressing the gap in early detection and treatment. This pursuit is driven by the potential of deep learning models, like DenseNet, to provide significant improvements in analyzing complex medical images for better clinical outcomes.

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Section: Introductionmentioning

confidence: 99%

Attention-Based DenseNet for Lung Cancer Classification Using CT Scan and Histopathological Images

Uddin

2024

Designs

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Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT)

Shah,

Khan,

Rizwan

et al. 2024

Pattern Anal Applic

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Cognitive disorders affect various cognitive functions that can have a substantial impact on individual’s daily life. Alzheimer’s disease (AD) is one of such well-known cognitive disorders. Early detection and treatment of cognitive diseases using artificial intelligence can help contain them. However, the complex spatial relationships and long-range dependencies found in medical imaging data present challenges in achieving the objective. Moreover, for a few years, the application of transformers in imaging has emerged as a promising area of research. A reason can be transformer’s impressive capabilities of tackling spatial relationships and long-range dependency challenges in two ways, i.e., (1) using their self-attention mechanism to generate comprehensive features, and (2) capture complex patterns by incorporating global context and long-range dependencies. In this work, a Bi-Vision Transformer (BiViT) architecture is proposed for classifying different stages of AD, and multiple types of cognitive disorders from 2-dimensional MRI imaging data. More specifically, the transformer is composed of two novel modules, namely Mutual Latent Fusion (MLF) and Parallel Coupled Encoding Strategy (PCES), for effective feature learning. Two different datasets have been used to evaluate the performance of proposed BiViT-based architecture. The first dataset contain several classes such as mild or moderate demented stages of the AD. The other dataset is composed of samples from patients with AD and different cognitive disorders such as mild, early, or moderate impairments. For comprehensive comparison, a multiple transfer learning algorithm and a deep autoencoder have been each trained on both datasets. The results show that the proposed BiViT-based model achieves an accuracy of 96.38% on the AD dataset. However, when applied to cognitive disease data, the accuracy slightly decreases below 96% which can be resulted due to smaller amount of data and imbalance in data distribution. Nevertheless, given the results, it can be hypothesized that the proposed algorithm can perform better if the imbalanced distribution and limited availability problems in data can be addressed. Graphical abstract

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Introducing a Novel Dataset for Facial Emotion Recognition and Demonstrating Significant Enhancements in Deep Learning Performance Through Preprocessing Techniques

Yalcin,

Alisawi

2024

Preprint

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Review of AlexNet for Medical Image Classification

Cited by 3 publications

References 86 publications

Attention-Based DenseNet for Lung Cancer Classification Using CT Scan and Histopathological Images

Attention-Based DenseNet for Lung Cancer Classification Using CT Scan and Histopathological Images

Computer-aided diagnosis of Alzheimer’s disease and neurocognitive disorders with multimodal Bi-Vision Transformer (BiViT)

Introducing a Novel Dataset for Facial Emotion Recognition and Demonstrating Significant Enhancements in Deep Learning Performance Through Preprocessing Techniques

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