Image Segmentation Based Survey on the Lung Cancer MRI Images

Perumal, S. Arumuga; Velmurugan, T.

doi:10.20894/ijdmta.102.005.002.015

Cited by 7 publications

(9 citation statements)

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“…To improve the quality of the MED-NODE, we used a MATLAB routine, histogram optimization, which enhanced the contrast of colored images. Image enhancement means improving an image’s perceptibility so that the final product is superior to the original: Image contrast enhancement before further pre-processing can improve analysis results [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the clinical images melanoma detection problem

Di Biasi,

De Marco,

Auriemma Citarella

et al. 2023

BMC Bioinformatics

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Background Melanoma is one of the deadliest tumors in the world. Early detection is critical for first-line therapy in this tumor pathology and it remains challenging due to the need for histological analysis to ensure correctness in diagnosis. Therefore, multiple computer-aided diagnosis (CAD) systems working on melanoma images were proposed to mitigate the need of a biopsy. However, although the high global accuracy is declared in literature results, the CAD systems for the health fields must focus on the lowest false negative rate (FNR) possible to qualify as a diagnosis support system. The final goal must be to avoid classification type 2 errors to prevent life-threatening situations. Another goal could be to create an easy-to-use system for both physicians and patients. Results To achieve the minimization of type 2 error, we performed a wide exploratory analysis of the principal convolutional neural network (CNN) architectures published for the multiple image classification problem; we adapted these networks to the melanoma clinical image binary classification problem (MCIBCP). We collected and analyzed performance data to identify the best CNN architecture, in terms of FNR, usable for solving the MCIBCP problem. Then, to provide a starting point for an easy-to-use CAD system, we used a clinical image dataset (MED-NODE) because clinical images are easier to access: they can be taken by a smartphone or other hand-size devices. Despite the lower resolution than dermoscopic images, the results in the literature would suggest that it would be possible to achieve high classification performance by using clinical images. In this work, we used MED-NODE, which consists of 170 clinical images (70 images of melanoma and 100 images of naevi). We optimized the following CNNs for the MCIBCP problem: Alexnet, DenseNet, GoogleNet Inception V3, GoogleNet, MobileNet, ShuffleNet, SqueezeNet, and VGG16. Conclusions The results suggest that a CNN built on the VGG or AlexNet structure can ensure the lowest FNR (0.07) and (0.13), respectively. In both cases, discrete global performance is ensured: 73% (accuracy), 82% (sensitivity) and 59% (specificity) for VGG; 89% (accuracy), 87% (sensitivity) and 90% (specificity) for AlexNet.

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Section: Methodsmentioning

confidence: 99%

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the clinical images melanoma detection problem

Di Biasi,

De Marco,

Auriemma Citarella

et al. 2023

BMC Bioinformatics

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“…Preprocessing serves as a pivotal phase in refining data for optimal training, employing techniques to enhance image quality by eliminating obstacles and irregularities [2]. This crucial step ensures that the data is appropriately normalized, contributing to effective training and achieving superior accuracy.…”

Section: Preprocessingmentioning

confidence: 99%

BISINDO (Bahasa Isyarat Indonesia) Sign Language Recognition Using Deep Learning

Setiawan,

Yunita,

Rahman

et al. 2024

itfs

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Sign language recognition plays a crucial role in facilitating communication for individuals with hearing impairments. This paper presents a deep learning-based approach for recognizing Bahasa Isyarat Indonesia (BISINDO), the sign language used in Indonesia. The proposed system employs convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to automatically extract features from sign language gestures and classify them into corresponding linguistic units. The dataset used for training and evaluation consists of annotated BISINDO sign language videos. Preprocessing techniques such as normalization and augmentation are applied to enhance the robustness of the model. Experimental results demonstrate the effectiveness of the proposed approach in accurately recognizing BISINDO sign language gestures, achieving state-of-the-art performance compared to existing methods. The developed system shows promising potential for real-world applications in enhancing communication accessibility for the hearing-impaired community in Indonesia.

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“…Resizing [69][70][71]. Te frst step is to adjust the image's dimensions while keeping its aspect ratio intact.…”

Section: 11mentioning

confidence: 99%

A Deep Learning System for Detecting Cardiomegaly Disease Based on CXR Image

Sorour,

Wafa,

Abohany

et al. 2024

International Journal of Intelligent Systems

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The potential of technology to revolutionize healthcare is exemplified by the synergy between artificial intelligence (AI) and early detection of cardiomegaly, demonstrating the power of proactive intervention in cardiovascular health. This paper presents an innovative approach that leverages advanced AI algorithms, specifically deep learning (DL) technology, for the early detection of cardiomegaly. The methodology consists of five key steps, including data collection, image preprocessing, data augmentation, feature extraction, and classification. Utilizing chest X-ray (CXR) images from the National Institutes of Health (NIH), the study applies rigorous image preprocessing operations, including color transformation and normalization. To enhance model generalization, data augmentation is employed, paving the way for two distinct DL models, a convolutional neural network (CNN) developed from scratch and a pretrained residual network with 50 layers (ResNet50), and adapted to the problem domain. Both models are systematically evaluated with five optimizers, revealing the AdaMax optimizer’s superiority for the CNN model and AdaGrad’s efficacy for the modified ResNet50. The proposed CNN with AdaMax achieves an impressive 99.91% accuracy, outperforming recent techniques in precision, recall, and F1−score. This research underscores the transformative potential of AI in cardiovascular health diagnostics, emphasizing the significance of timely intervention.

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Image Segmentation Based Survey on the Lung Cancer MRI Images

Cited by 7 publications

References 0 publications

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the clinical images melanoma detection problem

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the clinical images melanoma detection problem

BISINDO (Bahasa Isyarat Indonesia) Sign Language Recognition Using Deep Learning

A Deep Learning System for Detecting Cardiomegaly Disease Based on CXR Image

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