Reconstruction and Visualization of Protein Structures by exploiting Bidirectional Neural Networks and Discrete Classes

Citarella, Alessia Auriemma; Porcelli, Lorenzo; Biasi, Luigi Di; Risi, Michele; Tortora, Genoveffa

doi:10.1109/iv53921.2021.00053

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…With the development of artificial intelligence methods such as machine learning and deep learning, it is now feasible to assist clinicians with a wide range of activities. In order to extract relevant data for digital health, these cutting-edge technologies are increasingly applied to biomedical challenges [14,15], such as proteomics [16,17], genetics and image and signal data classification [18,19], and visualization [20]. Additionally, the Internet of Medical Things (IoMT), a subset of the Internet of Things (IoT) dedicated to the connectivity of all medical equipment, expands as more medical devices are connected [21].…”

Section: Why Cad For the Melanoma Detectionmentioning

confidence: 99%

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the Clinical Images Melanoma Detection Problem

Biasi

Marco

Citarella

et al. 2022

Preprint

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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: Why Cad For the Melanoma Detectionmentioning

confidence: 99%

Refactoring and performance analysis of the main CNN architectures: using false negative rate minimization to solve the Clinical Images Melanoma Detection Problem

Biasi

Marco

Citarella

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

ENTAIL: yEt aNoTher amyloid fIbrils cLassifier

et al. 2022

Self Cite

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Background This research aims to increase our knowledge of amyloidoses. These disorders cause incorrect protein folding, affecting protein functionality (on structure). Fibrillar deposits are the basis of some wellknown diseases, such as Alzheimer, Creutzfeldt–Jakob diseases and type II diabetes. For many of these amyloid proteins, the relative precursors are known. Discovering new protein precursors involved in forming amyloid fibril deposits would improve understanding the pathological processes of amyloidoses. Results A new classifier, called ENTAIL, was developed using over than 4000 molecular descriptors. ENTAIL was based on the Naive Bayes Classifier with Unbounded Support and Gaussian Kernel Type, with an accuracy on the test set of 81.80%, SN of 100%, SP of 63.63% and an MCC of 0.683 on a balanced dataset. Conclusions The analysis carried out has demonstrated how, despite the various configurations of the tests, performances are superior in terms of performance on a balanced dataset.

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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

Self Cite

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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.

show abstract

Reconstruction and Visualization of Protein Structures by exploiting Bidirectional Neural Networks and Discrete Classes

Cited by 3 publications

References 26 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

ENTAIL: yEt aNoTher amyloid fIbrils cLassifier

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

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