DenseNet-DC: Optimizing DenseNet Parameters Through Feature Map Generation Control

Most approaches use interactive priors to find tumours and then segment them based on tumour-centric candidates. A fully convolutional network is demonstrated for end-to-end breast tumour segmentation. When confronted with such a variety of options, to enhance tumour detection in digital mammograms, one uses multiscale picture information. Enhanced segmentation precision. The sampling of convolution layers are carefully chosen without adding parameters to prevent overfitting. The loss function is tuned to the tumor pixel fraction during training. Several studies have shown that the recommended method is effective. Tumour segmentation is automated for a variety of tumour sizes and forms postprocessing. Due to an increase in malignant cases, fundamental IoT malignant detection and family categorisation methodologies have been put to the test. In this paper, a novel malignant detection and family categorisation model based on the improved stochastic channel attention of convolutional neural networks (CNNs) is presented. The lightweight deep learning model complies with tougher execution, training, and energy limits in practice. The improved stochastic channel attention and DenseNet models are employed to identify malignant cells, followed by family classification. On our datasets, the proposed model detects malignant cells with 99.3 percent accuracy and family categorisation with 98.5 percent accuracy. The model can detect and classify malignancy.

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“…ere has been a lot of research done on machine and deep learning [18][19][20][21][22][23]. It is the categorisation and regression of the future.…”

Section: Related Workmentioning

confidence: 99%

“…e channel attention mechanism chooses which components to focus on. However, not every channel aids in picture recognition [20].…”

Section: Improved Stochastic Channel Attentionmentioning

confidence: 99%

A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer

Ramírez-Asís

Bolivar

Gonzales

et al. 2022

Computational Intelligence and Neuroscience

109

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Pathogen Detection in Dragonfruit With Transfer Learning and Fine-Tuned Keras Models

Asha Rani,

Gowrishankar

2024

Agriculture and Aquaculture Applications of Biosensors and Bioelectronics

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Dragonfruit, also known as pitaya, is a tropical fruit vulnerable to various diseases that can significantly impact its growth and yield. Timely and accurate disease detection and classification are crucial for effective management of dragonfruit cultivation. Transfer learning, a powerful machine learning technique, has shown promise in plant disease classification by leveraging pre-trained models. Keras, a popular deep learning library, offers pre-trained models suitable for image classification tasks. This research aims to develop a robust classification model for accurately identifying and distinguishing diseases by considering self-created dragonfruit plant dataset. By fine-tuning pre-trained Keras models, specifically adapted for dragonfruit disease classification, transfer learning is employed to achieve high accuracy in disease identification. The study contributes to agricultural disease detection by empowering farmers and researchers with a valuable tool for disease monitoring and management.

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DenseNet-DC: Optimizing DenseNet Parameters Through Feature Map Generation Control

Cited by 2 publications

References 17 publications

A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer

A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer

Pathogen Detection in Dragonfruit With Transfer Learning and Fine-Tuned Keras Models

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