SEGMENTACJA MAS NOWOTWOROWYCH NA OBRAZACH ULTRASONOGRAFII PIERSI Z UŻYCIEM ZMODYFIKOWANEGO MODELU U-NET

Breast cancer causes a huge number of women’s deaths every year. The accurate localization of a breast lesion is a crucial stage. The segmentation of breast ultrasound images participates in the improvement of the process of detection of breast anomalies. An automatic approach of segmentation of breast ultrasound images is presented in this paper, the proposed model is a modified u-net called Attention Residual U-net, designed to help radiologists in their clinical examination to determine adequately the limitation of breast tumors. Attention Residual U-net is a combination of existing models (Convolutional Neural Network U-net, the Attention Gate Mechanism and the Residual Neural Network). Public breast ultrasound images dataset of Baheya hospital in Egypt is used in this work. Dice coefficient, Jaccard index and Accuracy are used to evaluate the performance of the proposed model on the test set. Attention residual u-net can significantly give a dice coefficient = 90%, Jaccard index = 76% and Accuracy = 90%. The proposed model is compared with two other breast segmentation methods on the same dataset. The results show that the modified U-net model was able to achieve accurate segmentation of breast lesions in breast ultrasound images.Każdego roku rak piersi powoduje ogromną liczbę zgonów kobiet. Dokładna lokalizacja zmiany piersi jest kluczowym etapem. Segmentacja obrazów ultrasonograficznych piersi przyczynia się do poprawy procesu wykrywania nieprawidłowości piersi. W tym artykule przedstawiono automatyczne podejście do segmentacji obrazów ultrasonograficznych piersi, proponowany model to zmodyfikowany U-net, nazwany Attention Residual U-net, zaprojektowany w celu wspomagania radiologów podczas badania klinicznego, w celu odpowiedniego określenia zasięgu guzów piersiowych. Attention Residual U-net jest połączeniem istniejących modeli (konwolucyjną siecią neuronową U-net, Attention Gate Mechanism  i Residual Neural Network). W tym badaniu wykorzystano publiczny zbiór danych obrazów ultrasonograficznych piersi szpitala Baheya w Egipcie. Do oceny wydajności zaproponowanego modelu na zbiorze testowym wykorzystano współczynnik Dice'a, indeks Jaccarda i dokładność. Attention Residual U-net może znacznie przyczynić się do uzyskania współczynnika Dice'a równego 90%, indeksu Jaccarda równego 76% i dokładności równiej 90%. Proponowany model został porównany z dwoma innymi metodami segmentacji piersi na tym samym zbiorze danych. Wyniki pokazują, że zmodyfikowany model U-net był w stanie osiągnąć dokładną segmentację zmian piersiowych na obrazach ultrasonograficznych piersi

Comprehensive Cardiac Ischemia Classification Using Hybrid CNN-Based Models

This study addresses the critical issue of classifying cardiac ischemia, a disease with significant global health implications that contributes to the global mortality rate. In our study, we tackle the classification of ischemia using six diverse electrocardiogram (ECG) datasets and a convolutional neural network (CNN) as the primary methodology. We combined six separate datasets to gain a more comprehensive understanding of cardiac electrical activity, utilizing 12 leads to obtain a broader perspective. A discrete wavelet transform (DWT) preprocessing was used to eliminate irrelevant information from the signals, aiming to improve classification results. Focusing on accuracy and minimizing false negatives (FN) in ischemia detection, we enhance our study by incorporating various machine learning models into our base model. These models include multilayer perceptron (MLP), support vector machines (SVM), random forest (RF), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), allowing us to leverage the strengths of each algorithm. The CNN-BiLSTM model achieved the highest accuracy of 99.23% and demonstrated good sensitivity of 98.53%, effectively reducing false negative cases in the overall tests. The CNN-BiLSTM model demonstrated the ability to effectively identify abnormalities, misclassifying only 25 out of 1,673 ischemic cases in the test set as normal. This is due to the BiLSTM’s efficiency in capturing long-range dependencies and sequential patterns, making it suitable for tasks involving time-series data such as ECG signals. In addition, CNNs are well-suited for hierarchical feature learning and complex pattern recognition in ECG data

Computer Vision-Based Approach for Automated Monitoring and Assessment of Gait Rehabilitation at Home

This study presents a markerless video-based human gait analysis system for automatic assessment of at-home rehabilitation. A marker-based MoCap system (Vicon) is used to evaluate the accuracy of the proposed approach. Additionally, a novel gait rehabilitation score based on the Dynamic Time Warping (DTW) algorithm is introduced, enabling quantification of rehabilitation progress. The accuracy of the proposed approach is assessed by comparing it to a marker-based MoCap system (Vicon), which is used to evaluate the proposed approach. This evaluation results in mean absolute errors (MAE) of 4.8° and 5.2° for the left knee, and 5.9° and 5.7° for the right knee, demonstrating an acceptable accuracy in knee angle measurements. The obtained scores effectively distinguish between normal and abnormal gait patterns. Subjects with normal gait exhibit scores around 97.5%, 98.8%, while those with abnormal gait display scores around 30%, 29%, respectively. Furthermore, a subject at an advanced stage of rehabilitation achieved a score of 65%. These scores provide valuable insights for patients, allowing them to assess their rehabilitation progress and distinguish between different levels of gait recovery. The proposed markerless approach demonstrates acceptable accuracy in measuring knee joint angles during a sagittal walk and provides a reliable rehabilitation score, making it a convenient and cost-effective alternative for automatic at-home rehabilitation monitoring

IoT DO PREDYKCYJNEJ KONSERWACJI KRYTYCZNEGO SPRZĘTU MEDYCZNEGO W STRUKTURZE SZPITALA

Predictive maintenance (PdM) allows the prediction of early failures of medical equipment before they occur. It helps to diagnose the defaults of critical equipment in a hospital structure, namely MRI. Founded on the analysis of data collected in real time of the right parameters, thanks to intelligent sensors positioned on the equipment, using Internet of Things (IoT) technology and the practice of machine learning tools. The objective of this techniques is the implementation of algorithms capable to predict an anomaly, which will make equipment and maintenance tools increasingly autonomous and intelligent. Therefore, the idea of this project is to develop a wireless sensor network to ensure continuous monitoring of the state of MRI. The implemented solution includes an IoT monitoring system of the cold head’s cooling circuit. Based on the vibrations at the pump, it allows to monitor the motor circuit, inform the staff at each abnormal state of this system, and protect this device against any future anomalies. Thanks to the CNN algorithm implemented in this solution, the results are very satisfactory, with an accuracy >98%. This solution can be integrated into a general predictive maintenance solution for the most sensitive equipment in a hospital.Konserwacja predykcyjna (PdM) umożliwia przewidywanie wczesnych awarii sprzętu medycznego przed ich wystąpieniem. Pomaga zdiagnozować usterki krytycznego sprzętu w strukturze szpitala, na przykład MRI. Opiera się na analizie danych zbieranych w czasie rzeczywistym odpowiednich parametrów, dzięki inteligentnym czujnikom umieszczonym na sprzęcie, przy użyciu technologii Internetu rzeczy (IoT) i narzędzi uczenia maszynowego. Celem tych technik jest wdrożenie algorytmów zdolnych do przewidywania anomalii, które sprawią, że sprzęt i narzędzia konserwacyjne będą coraz bardziej autonomiczne i inteligentne. Dlatego ideą tego projektu jest opracowanie bezprzewodowej sieci czujników w celu zapewnienia ciągłego monitorowania stanu MRI. Wdrożone rozwiązanie obejmuje system monitorowania IoT obwodu chłodzenia zimnej głowicy. W oparciu o wibracje pompy pozwala on monitorować obwód silnika, informować personel o każdym nieprawidłowym stanie tego systemu i chronić to urządzenie przed wszelkimi przyszłymi anomaliami. Dzięki algorytmowi CNN zaimplementowanemu w tym rozwiązaniu, wyniki są bardzo zadowalające, z dokładnością >98%. Rozwiązanie to można zintegrować z ogólnym rozwiązaniem konserwacji predykcyjnej dla najbardziej wrażliwego sprzętu w szpitalu

SEGMENTATION OF CANCER MASSES ON BREAST ULTRASOUND IMAGES USING MODIFIED U-NET

Breast cancer causes a huge number of women’s deaths every year. The accurate localization of a breast lesion is a crucial stage. The segmentation of breast ultrasound images participates in the improvement of the process of detection of breast anomalies. An automatic approach of segmentation of breast ultrasound images is presented in this paper, the proposed model is a modified u-net called Attention Residual U-net, designed to help radiologists in their clinical examination to determine adequately the limitation of breast tumors. Attention Residual U-net is a combination of existing models (Convolutional Neural Network U-net, the Attention Gate Mechanism and the Residual Neural Network). Public breast ultrasound images dataset of Baheya hospital in Egypt is used in this work. Dice coefficient, Jaccard index and Accuracy are used to evaluate the performance of the proposed model on the test set. Attention residual u-net can significantly give a dice coefficient = 90%, Jaccard index = 76% and Accuracy = 90%. The proposed model is compared with two other breast segmentation methods on the same dataset. The results show that the modified U-net model was able to achieve accurate segmentation of breast lesions in breast ultrasound images

U-Net transfer learning backbones for lesions segmentation in breast ultrasound images

Breast ultrasound images are highly valuable for the early detection of breast cancer. However, the drawback of these images is low-quality resolution and the presence of speckle noise, which affects their interpretability and makes them radiologists’ expertise-dependent. As medical images, breast ultrasound datasets are scarce and imbalanced, and annotating them is tedious and time-consuming. Transfer learning, as a deep learning technique, can be used to overcome the dataset deficiency in available images. This paper presents the implementation of transfer learning U-Net backbones for the automatic segmentation of breast ultrasound lesions and implements a threshold selection mechanism to deliver optimal generalized segmentation results of breast tumors. The work uses the public breast ultrasound images (BUSI) dataset and implements ten state-of-theart candidate models as U-Net backbones. We have trained these models with a five-fold cross-validation technique on 630 images with benign and malignant cases. Five out of ten models showed good results, and the best U-Net backbone was found to be DenseNet121. It achieved an average Dice coefficient of 0.7370 and a sensitivity of 0.7255. The model’s robustness was also evaluated against normal cases, and the model accurately detected 72 out of 113 images, which is higher than the four best models

OPTYMALIZACJA KLASYFIKACJI OBRAZÓW ULTRASONOGRAFICZNYCH TECHNIKĄ TRANSFER LEARNING: STRATEGIE DOSTRAJANIA I WPŁYW KLASYFIKATORA NA WSTĘPNIE WYTRENOWANE WARSTWY WEWNĘTRZNE

Transfer Learning (TL) is a popular deep learning technique used in medical image analysis, especially when data is limited. It leverages pre-trained knowledge from State-Of-The-Art (SOTA) models and applies it to specific applications through Fine-Tuning (FT). However, fine-tuning large models can be time-consuming, and determining which layers to use can be challenging. This study explores different fine-tuning strategies for five SOTA models (VGG16, VGG19, ResNet50, ResNet101, and InceptionV3) pre-trained on ImageNet. It also investigates the impact of the classifier by using a linear SVM for classification. The experiments are performed on four open-access ultrasound datasets related to breast cancer, thyroid nodules cancer, and salivary glands cancer. Results are evaluated using a five-fold stratified cross-validation technique, and metrics like accuracy, precision, and recall are computed. The findings show that fine-tuning 15% of the last layers in ResNet50 and InceptionV3 achieves good results. Using SVM for classification further improves overall performance by 6% for the two best-performing models. This research provides insights into fine-tuning strategies and the importance of the classifier in transfer learning for ultrasound image classification.Transfer Learning (TL) to popularna technika głębokiego uczenia stosowana w analizie obrazów medycznych, zwłaszcza gdy ilość danych jest ograniczona. Wykorzystuje ona wstępnie wyszkoloną wiedzę z modeli State-Of-The-Art (SOTA) i zastosowanie ich do konkretnych aplikacji poprzez dostrajanie (Fine-Tuning – FT). Jednak dostrajanie dużych modeli może być czasochłonne, a określenie, których warstw użyć, może stanowić wyzwanie. W niniejszym badaniu przeanalizowano różne strategie dostrajania dla pięciu modeli SOTA (VGG16, VGG19, ResNet50, ResNet101 i InceptionV3) wstępnie wytrenowanych na ImageNet. Zbadano również wpływ klasyfikatora przy użyciu liniowej SVM do klasyfikacji. Eksperymenty przeprowadzono na czterech ogólnodostępnych zbiorach danych ultrasonograficznych związanych z rakiem piersi, rakiem guzków tarczycy i rakiem gruczołów ślinowych. Wyniki są oceniane przy użyciu techniki pięciowarstwowej walidacji krzyżowej, a wskaźniki takie jak dokładność, precyzja i odzyskiwanie są obliczane. Wyniki pokazują, że dostrojenie 15% ostatnich warstw w ResNet50 i InceptionV3 osiąga dobre wyniki. Użycie SVM do klasyfikacji dodatkowo poprawia ogólną wydajność o 6% dla dwóch najlepszych modeli. Badania te zapewniają informacje na temat strategii dostrajania i znaczenia klasyfikatora w uczeniu transferowym dla klasyfikacji obrazów ultrasonograficznych

Segmentacja mas nowotworowych na obrazach ultrasonografii piersi z użyciem zmodyfikowanego modelu U-net

Breast cancer causes a huge number of women’s deaths every year. The accurate localization of a breast lesion is a crucial stage. The segmentation of breast ultrasound images participates in the improvement of the process of detection of breast anomalies. An automatic approach of segmentation of breast ultrasound images is presented in this paper, the proposed model is a modified u-net called Attention Residual U-net, designed to help radiologists in their clinical examination to determine adequately the limitation of breast tumors. Attention Residual U-net is a combination of existing models (Convolutional Neural Network U-net, the Attention Gate Mechanism and the Residual Neural Network). Public breast ultrasound images dataset of Baheya hospital in Egypt is used in this work. Dice coefficient, Jaccard index and Accuracy are used to evaluate the performance of the proposed model on the test set. Attention residual u-net can significantly give a dice coefficient = 90%, Jaccard index = 76% and Accuracy = 90%. The proposed model is compared with two other breast segmentation methods on the same dataset. The results show that the modified U-net model was able to achieve accurate segmentation of breast lesions in breast ultrasound images.Każdego roku rak piersi powoduje ogromną liczbę zgonów kobiet. Dokładna lokalizacja zmiany piersi jest kluczowym etapem. Segmentacja obrazów ultrasonograficznych piersi przyczynia się do poprawy procesu wykrywania nieprawidłowości piersi. W tym artykule przedstawiono automatyczne podejście do segmentacji obrazów ultrasonograficznych piersi, proponowany model to zmodyfikowany U-net, nazwany Attention Residual U-net, zaprojektowany w celu wspomagania radiologów podczas badania klinicznego, w celu odpowiedniego określenia zasięgu guzów piersiowych. Attention Residual U-net jest połączeniem istniejących modeli (konwolucyjną siecią neuronową U-net, Attention Gate Mechanism i Residual Neural Network). W tym badaniu wykorzystano publiczny zbiór danych obrazów ultrasonograficznych piersi szpitala Baheya w Egipcie. Do oceny wydajności zaproponowanego modelu na zbiorze testowym wykorzystano współczynnik Dice'a, indeks Jaccarda i dokładność. Attention Residual U-net może znacznie przyczynić się do uzyskania współczynnika Dice'a równego 90%, indeksu Jaccarda równego 76% i dokładności równiej 90%. Proponowany model został porównany z dwoma innymi metodami segmentacji piersi na tym samym zbiorze danych. Wyniki pokazują, że zmodyfikowany model U-net był w stanie osiągnąć dokładną segmentację zmian piersiowych na obrazach ultrasonograficznych piersi

Vigilance towards the use of artificial intelligence applications for breast cancer screening and early diagnosis

Breast cancer is a real public health problem in Morocco. It is the cause of a significant number of deaths caused by late diagnosis. Mammography plays an essential role in the detection of breast cancer and in the early management of its treatment. Despite the existence of screening programs, there are still high rates of false positives and false negatives. Indeed, women were called back for additional diagnoses based on suspicious results that eventually led to cancer. Artificial intelligence (AI) algorithms represent a promising solution to improve the accuracy of digital mammography offering, on the one hand, the possibility of better cancer detection, and, on the other hand, improved efficiency for radiologists for good decision-making. In this work, through a review of the literature on the tools used to evaluate the performance of AI systems dedicated to early detection and diagnosis of breast cancer. We set out to answer the following questions: Is the ethics relating to patient data during the development phase of this software is respected? Do these tools take into consideration the specificities of the field? What about the specification, accuracy and limitations of these applications? At the end, we show through this work recommendations to adapt these evaluation tools of AI applications for breast cancer screening for an optimized and rational consideration of the principle of health vigilance and compliance with the regulatory standards in force governing this field

Identifying Retinal Diseases on OCT Image Based on Deep Learning

Computer-aided diagnosis has the potential to replace or at least support medical personnel in their everyday responsibilities such as diagnosis, therapy, and surgery. In the area of ophthalmology, artificial intelligence approaches have been incorporated in the diagnosis of the most frequent ocular disorders, such as choroidal neovascularization (CNV), diabetic macular oedema (DMO), and DRUSEN; these illnesses pose a significant risk of vision loss. Optical coherence tomography (OCT) is an imaging technology used to diagnose the aforementioned eye disorders. It enables ophthalmologists to see the back of the eye and take various slices of the retina. The goal of this research is to automate the diagnosis of retinopathy, which includes CNV, DME, and DRUSEN. The approach employed is a deep learning-based, and transfer learning technique, applying to a public dataset of OCT pictures and two pertained neural network models VGG16 and InceptionV3, which are trained on the big database "ImageNet." That allows them to be able to extract the main features of millions of images. Furthermore, fine-tuning approaches are applied to outperform the feature extraction method, by modifying the hyperparameters. The findings showed that the VGG16 model performed better in classification than the InceptionV3 architecture, with a 0.93 accuracy