Wyniki wyszukiwania - BazTech

1

A comprehensive study: intracranial aneurysm detection via VGG16-Densenet hybrid deep learning on DSA images

Mummaneni Sobhana, Ravi Sasi Tilak, Bodedla Jashwanth, Vemulapalli Sree Ram, Jagathapurao Kowshik Varma

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

|

2024

|

T. 14, nr 1

105--110

EN

An intracranial aneurysm is a swelling in a weak area of a brain artery. The main cause of aneurysm is high blood pressure, smoking, and head injury. A ruptured aneurysm is a serious medical emergency that can lead to coma and then death. A digital subtraction angiogram (DSA) is used to detect a brain aneurysm. A neurosurgeon carefully examines the scan to find the exact location of the aneurysm. A hybrid model has been proposed to detect these aneurysms accurately and quickly. Visual Geometry Group 16 (VGG16) and DenseNet are two deep-learning architectures used for image classification. Ensembling both models opens the possibility of using diversity in a robust and stable feature extraction. The model results assist in identifying the location of aneurysms, which are much less prone to false positives or false negatives. This integration of a deep learning-based architecture into medical practice holds great promise for the timely and accurate detection of aneurysms. The study encompasses 1654 DSA images from distinct patients, partitioned into 70% for training (1157 images) and 30% for testing (496 images). The ensembled model manifests an impressive accuracy of 95.38%, outperforming the respective accuracies of VGG16 (94.38%) and DenseNet (93.57%). Additionally, the ensembled model achieves a recall value of 0.8657, indicating its ability to correctly identify approximately 86.57% of true aneurysm cases out of all actual positive cases present in the dataset. Furthermore, when considering DenseNet individually, it attains a recall value of 0.8209, while VGG16 attains a recall value of 0.8642. These values demonstrate the sensitivity of each model to detecting aneurysms, with the ensemble model showcasing superior performance compared to its individual components.

PL

Tętniak wewnątrzczaszkowy to obrzęk w słabym obszarze tętnicy mózgowej. Główną przyczyną tętniaka jest wysokie ciśnienie krwi, palenie tytoniu i uraz głowy. Pęknięcie tętniaka jest poważnym stanem nagłym, który może prowadzić do śpiączki, a następnie śmierci. W celu wykrycia tętniaka mózgu stosuje się cyfrową angiografię subtrakcyjną (DSA). Neurochirurg dokładnie bada skan, aby znaleźć dokładną lokalizację tętniaka. Zaproponowano model hybrydowy do dokładnego i szybkiego wykrywania tych tętniaków. Visual Geometry Group 16 (VGG16) i DenseNet to dwie architektury głębokiego uczenia wykorzystywane do klasyfikacji obrazów. Połączenie obu modeli otwiera możliwość wykorzystania różnorodności w solidnej i stabilnej ekstrakcji cech. Wyniki modelu pomagają w identyfikacji lokalizacji tętniaków, które są znacznie mniej podatne na fałszywie dodatnie lub fałszywie ujemne. Ta integracja architektury opartej na głębokim uczeniu się z praktyką medyczną jest bardzo obiecująca dla szybkiego i dokładnego wykrywania tętniaków. Badanie obejmuje 1654 obrazów DSA od różnych pacjentów, podzielonych na 70% do treningu (1157 obrazów) i 30% do testowania (496 obrazów). Złożony model wykazuje imponującą dokładność 95,38%, przewyższając odpowiednie dokładności VGG16 (94,38%) i DenseNet (93,57%). Dodatkowo, złożony model osiąga wartość pełności 0,8657, co wskazuje na jego zdolność do prawidłowej identyfikacji około 86,57% prawdziwych przypadków tętniaka spośród wszystkich rzeczywistych pozytywnych przypadków obecnych w zbiorze danych. Ponadto, biorąc pod uwagę DenseNet indywidualnie, osiąga on wartość pełności 0,8209, podczas gdy VGG16 osiąga wartość pełności 0,8642. Wartości te pokazują czułość każdego modelu w wykrywaniu tętniaków, przy czym model zespołowy wykazuje lepszą wydajność w porównaniu z jego poszczególnymi komponentami.

2

Classification and segmentation of periodontal cystfor digital dental diagnosis using deep learning

Lakshmi T. K., Dheeba J.

Computer Assisted Methods in Engineering and Science

|

2023

|

Vol. 30, no. 2

131--149

EN

The digital revolution is changing every aspect of life by simulating the ways humansthink, learn and make decisions. Dentistry is one of the major fields where subsets ofartificial intelligence are extensively used for disease predictions. Periodontitis, the mostprevalent oral disease, is the main focus of this study. We propose methods for classifyingand segmenting periodontal cysts on dental radiographs using CNN, VGG16, and U-Net.Accuracy of 77.78% is obtained using CNN, and enhanced accuracy of 98.48% is obtainedthrough transfer learning with VGG16. The U-Net model also gives encouraging results.This study presents promising results, and in the future, the work can be extended withother pre-trained models and compared. Researchers working in this field can develop novelmethods and approaches to support dental practitioners and periodontists in decision-making and diagnosis and use artificial intelligence to bridge the gap between humansand machines.

3

A DHCR_ SmartNet: A smart Devanagari handwritten character recognition using level-wised CNN architecture

Deore Shalaka Prasad

Computer Science

|

2022

|

T. 23 (3)

301--320

EN

Handwritten script recognition is a vital application of the machine-learning domain. Applications like automatic license plate detection, pin-code detection, and historical document management increases attention toward handwritten script recognition. English is the most widely spoken language in India; hence, there has been a lot of research into identifying a script using a machine. Devanagari is a popular script that is used by a large number of people on the Indian subcontinent. In this paper, a level-wised efficient transfer-learning approach is presented on the VGG16 model of a convolutional neural network (CNN) for identifying isolated Devanagari handwritten characters. In this work, a new dataset of Devanagari characters is presented and made accessible to the public. This newly created dataset is comprised of 5800 samples for 12 vowels, 36 consonants, and 10 digits. Initially, a simple CNN is implemented and trained on this new small dataset. During the next stage, a transfer-learning approach is implemented on the VGG16 model, and during the last stage, the efficient fine-tuned VGG16 model is implemented. The obtained accuracy of the fine-tuned model’s training and testing came to 98.16% and 96.47%, respectively.

4

Automated detection of COVID-19 from CT scan using convolutional neural network

Mishra Narendra Kumar, Singh Pushpendra, Joshi Shiv Dutt

Biocybernetics and Biomedical Engineering

|

2021

|

Vol. 41, no. 2

572--588

EN

Under the prevailing circumstances of the global pandemic of COVID-19, early diagnosis and accurate detection of COVID-19 through tests/screening and, subsequently, isolation of the infected people would be a proactive measure. Artificial intelligence (AI) based solutions, using Convolutional Neural Network (CNN) and exploiting the Deep Learning model’s diagnostic capabilities, have been studied in this paper. Transfer Learning approach, based on VGG16 and ResNet50 architectures, has been used to develop an algorithm to detect COVID-19 from CT scan images consisting of Healthy (Normal), COVID-19, and Pneumonia categories. This paper adopts data augmentation and fine-tuning techniques to improve and optimize the VGG16 and ResNet50 model. Further, stratified 5-fold cross-validation has been conducted to test the robustness and effectiveness of the model. The proposed model performs exceptionally well in case of binary classification (COVID-19 vs. Normal) with an average classification accuracy of more than 99% in both VGG16 and ResNet50 based models. In multiclass classification (COVID-19 vs. Normal vs. Pneumonia), the proposed model achieves an average classification accuracy of 86.74% and 88.52% using VGG16 and ResNet50 architectures as baseline, respectively. Experimental results show that the proposed model achieves superior performance and can be used for automated detection of COVID-19 from CT scans.