HS‐UNET‐ID: An approach for human skin classification integrating between UNET and improved dense convolutional network

Nguyen, Hai Thanh; Luong, Huong Hoang; Phan, Phat Tan; Nguyen, Hung Huy Duc; Ly, Duong; Phan, Duc Minh; Trung, Tin

doi:10.1002/ima.22776

Cited by 4 publications

(3 citation statements)

References 64 publications

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“…Processing medical images by automatic segmentation and classification becoming extremely important around the world [16], [17], especially in the medical field such as diagnostics, growth prediction, and treatment of brain tumors. As a result, a patient can save their life because an early detection of brain tumors that helps to increase their survival rate.…”

Section: Related Workmentioning

confidence: 99%

Improving Brain Tumor MRI Image Classification Prediction based on Fine-tuned MobileNet

Lu,

Nguyen,

Lam

2024

IJACSA

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Brain tumors are a prevalent issue in contemporary society as they impact human health. The location of the tumor in the brain determines the variety of symptoms that may manifest. Some frequent symptoms are cephalalgia, convulsions, visual impairments, nausea, emesis, asthenia, paresthesia, dysphasia, personality alterations, and amnesia. The prognosis for brain cancer differs considerably depending on the cancer type. Nevertheless, brain tumors are amenable to treatment with surgical intervention, chemotherapy, and radiotherapy if the diagnosis is timely. Furthermore, artificial intelligence and machine learning can assist in the detection of brain tumors as they have significant implications for the analysis of Magnetic Resonance Imaging (MRI). To accomplish this objective, automated measurement instruments were proposed based on the processing of MRI. In this study, we employed the latest developments in deep transfer learning and fine-tuning to identify tumors without many complex steps. We gathered data from authentic MRI of 3264 subjects (i.e., 926 glioma tumors, 937 meningioma tumors, 901 pituitary tumors, and 500 normal). With the MobileNet model from the Keras library, we attained the highest validation accuracy, test accuracy, and F1 score in four-class classifications was 97.24%, 97,86%, and 97.85%, respectively. Concerning two-class classification, high accuracy values were obtained for most of the models (i.e., ~100%). These outcomes and other performance indicators demonstrate a strong capability to diagnose brain tumors from conventional MRI. The current research developed a supportive machine learning that can aid doctors in making the accurate diagnosis with less time and mistakes.

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Section: Related Workmentioning

confidence: 99%

Improving Brain Tumor MRI Image Classification Prediction based on Fine-tuned MobileNet

Lu,

Nguyen,

Lam

2024

IJACSA

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“…The ViT16 model, in particular, is a variant of the Vision Transformer architecture that has demonstrated impressive performance in various computer vision tasks. It consists of 16 Transformer blocks, each with selfattention layers and feed-forward neural networks. Through extensive training on large-scale datasets, ViT16 has learned to extract informative features from input images and make accurate predictions.…”

Section: Introductionmentioning

confidence: 99%

Improving Chicken Disease Classification Based on Vision Transformer and Combine with Integrated Gradients Explanation

Luong,

Nguyen

2024

IJACSA

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Chicken diseases are an important problem in the livestock industry, affecting the health and production performance of chicken flocks worldwide. These diseases can seriously damage the health of chickens, reduce egg production, or increase mortality, causing great economic losses to farmers. Therefore, detecting and preventing diseases in chickens is a top concern in the livestock industry, to ensure the health and sustainable production of chicken flocks. In recent years, advances in machine learning techniques have shown promise in solving challenges related to image diagnosis and classification. Leveraging the power of machine learning models, we propose the ViT16 model for disease classification in chickens, demonstrating its potential in assisting healthcare professionals to diagnose chicken flocks more effectively. In this study, ViT16 demonstrated its potential and strengths when compared with 5 models in the CNN architecture and ViT32 in the ViT architecture in the task of classifying chicken disease images with an accuracy of 99.25% -99.75% -100% -98.25% in four experimental scenarios with our enhanced dataset and fine-tuning. These results were generated from transfer learning and model tuning on an augmented dataset consisting of 8067 images classified into four classes: Coccidiosis, New Castle Disease, Salmonella, and Healthy. Furthermore, the Integrated Gradients explanation has an important role in increasing the transparency and understanding of the image classification model, thereby improving and optimizing model performance. The performance evaluation of each model is done through in-depth analysis, including metrics such as precision, recall, F1 score, accuracy, and confusion matrix.

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“…Unet and its variant models continue to be the preferred method in the medical imaging domain owing to the fewer parameters involved when compared to dense-connection models. The Unet and variant models have thus far been applied to a variety of medical imaging use cases, from dental segmentation in [9] and human skin classification in [10] to polyp segmentation tasks in colonoscopy images in [11]. This demonstrates the versatility of the Unet and variant models in the medical imaging domain, thereby necessitating the development of advanced Unet model versions.…”

Section: Introductionmentioning

confidence: 99%

NUMSnet: Nested-U Multi-Class Segmentation Network for 3D Medical Image Stacks

Roychowdhury

2023

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The semantic segmentation of 3D medical image stacks enables accurate volumetric reconstructions, computer-aided diagnostics and follow-up treatment planning. In this work, we present a novel variant of the Unet model, called the NUMSnet, that transmits pixel neighborhood features across scans through nested layers to achieve accurate multi-class semantic segmentation with minimal training data. We analyzed the semantic segmentation performance of the NUMSnet model in comparison with several Unet model variants in the segmentation of 3–7 regions of interest using only 5–10% of images for training per Lung-CT and Heart-CT volumetric image stack. The proposed NUMSnet model achieves up to 20% improvement in segmentation recall, with 2–9% improvement in Dice scores for Lung-CT stacks and 2.5–16% improvement in Dice scores for Heart-CT stacks when compared to the Unet++ model. The NUMSnet model needs to be trained with ordered images around the central scan of each volumetric stack. The propagation of image feature information from the six nested layers of the Unet++ model are found to have better computation and segmentation performance than the propagation of fewer hidden layers or all ten up-sampling layers in a Unet++ model. The NUMSnet model achieves comparable segmentation performance to previous works while being trained on as few as 5–10% of the images from 3D stacks. In addition, transfer learning allows faster convergence of the NUMSnet model for multi-class semantic segmentation from pathology in Lung-CT images to cardiac segmentation in Heart-CT stacks. Thus, the proposed model can standardize multi-class semantic segmentation for a variety of volumetric image stacks with a minimal training dataset. This can significantly reduce the cost, time and inter-observer variability associated with computer-aided detection and treatment.

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HS‐UNET‐ID: An approach for human skin classification integrating between UNET and improved dense convolutional network

Cited by 4 publications

References 64 publications

Improving Brain Tumor MRI Image Classification Prediction based on Fine-tuned MobileNet

Improving Brain Tumor MRI Image Classification Prediction based on Fine-tuned MobileNet

Improving Chicken Disease Classification Based on Vision Transformer and Combine with Integrated Gradients Explanation

NUMSnet: Nested-U Multi-Class Segmentation Network for 3D Medical Image Stacks

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