Automated Breast Cancer Detection Models Based on Transfer Learning

Breast cancer is among the leading causes of mortality for females across the planet. It is essential for the well-being of women to develop early detection and diagnosis techniques. In mammography, focus has contributed to the use of deep learning (DL) models, which have been utilized by radiologists to enhance the needed processes to overcome the shortcomings of human observers. The transfer learning method is being used to distinguish malignant and benign breast cancer by fine-tuning multiple pre-trained models. In this study, we introduce a framework focused on the principle of transfer learning. In addition, a mixture of augmentation strategies were used to prevent overfitting and produce stable outcomes by increasing the number of mammographic images; including several rotation combinations, scaling, and shifting. On the Mammographic Image Analysis Society (MIAS) dataset, the proposed system was evaluated and achieved an accuracy of 89.5% using (residual network-50) ResNet50, and achieved an accuracy of 70% using the Nasnet-Mobile network. The proposed system demonstrated that pre-trained classification networks are significantly more effective and efficient, making them more acceptable for medical imaging, particularly for small training datasets

CT findings of the commonly overlooked groove pancreatitis

Purpose: The aim of this study was to highlight the different computed tomography (CT) features of groove pancreatitis (GP) in order to make this entity more familiar to radiologist. Patients & method: This study enrolled 15 patients who had histopathologically confirmed GP. Their CT scans were retrospectively reviewed for the encountered manifestations. Results: Pure & segmental forms were identified retrospectively in 6 & 9 patients. The most frequent findings noted in patients' scans were the following, in descending order: medial duodenal wall thickening & cysts, duodenal luminal narrowing, regional lymphadenopathies, pancreatic involvement, isolated groove affection, pancreatic calcifications, distal CBD narrowing, pancreatic duct abnormalities, and retro-peritoneal stranding. Conclusion: Although the CT features of GP mimic other peripancreatic tumors, yet the constantly associated findings in the proven cases of GP in our study were: duodenal wall thickening, cysts formation, and luminal narrowing. So the presence of these features in alcoholic middle aged male patient with groove or pancreatic lesion, have to trigger radiologist's dubiety of GP entity and so to be addressed in his opinion. Nevertheless, GP diagnosis is still challenging & should be considered based on clinical & radiological data in conjunction with the laboratory and pathological results

Enhancing diabetic retinopathy classification using deep learning

Prolonged hyperglycemia can cause diabetic retinopathy (DR), which is a major contributor to blindness. Numerous incidences of DR may be avoided if it were identified and addressed promptly. Throughout recent years, many deep learning (DL)-based algorithms have been proposed to facilitate psychometric testing. Utilizing DL model that encompassed four scenarios, DR and its stages were identified in this study using retinal scans from the “Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 Blindness Detection” dataset. Adopting a DL model then led to the use of augmentation strategies that produced a comprehensive dataset with consistent hyper parameters across all test cases. As a further step in the classification process, we used a Convolutional Neural Network model. Different enhancement methods have been used to raise visual quality. The proposed approach detected the DR with a highest experimental result of 97.83%, a top-2 accuracy of 99.31%, and a top-3 accuracy of 99.88% across all the 5 severity stages of the APTOS 2019 evaluation employing CLAHE and ESRGAN techniques for image enhancement. In addition, we employed APTOS 2019 to develop a set of evaluation metrics (precision, recall, and F1-score) to use in analyzing the efficacy of the suggested model. The proposed approach was also proven to be more efficient at DR location than both state-of-the-art technology and conventional DL

Melanoma Detection Using Deep Learning-Based Classifications

One of the most prevalent cancers worldwide is skin cancer, and it is becoming more common as the population ages. As a general rule, the earlier skin cancer can be diagnosed, the better. As a result of the success of deep learning (DL) algorithms in other industries, there has been a substantial increase in automated diagnosis systems in healthcare. This work proposes DL as a method for extracting a lesion zone with precision. First, the image is enhanced using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the image’s quality. Then, segmentation is used to segment Regions of Interest (ROI) from the full image. We employed data augmentation to rectify the data disparity. The image is then analyzed with a convolutional neural network (CNN) and a modified version of Resnet-50 to classify skin lesions. This analysis utilized an unequal sample of seven kinds of skin cancer from the HAM10000 dataset. With an accuracy of 0.86, a precision of 0.84, a recall of 0.86, and an F-score of 0.86, the proposed CNN-based Model outperformed the earlier study’s results by a significant margin. The study culminates with an improved automated method for diagnosing skin cancer that benefits medical professionals and patients

Detection of COVID-19 Based on Chest X-rays Using Deep Learning

The coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease’s spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies. Two different DL approaches based on a pertained neural network model (ResNet-50) for COVID-19 detection using chest X-ray (CXR) images are proposed in this study. Augmenting, enhancing, normalizing, and resizing CXR images to a fixed size are all part of the preprocessing stage. This research proposes a DL method for classifying CXR images based on an ensemble employing multiple runs of a modified version of the Resnet-50. The proposed system is evaluated against two publicly available benchmark datasets that are frequently used by several researchers: COVID-19 Image Data Collection (IDC) and CXR Images (Pneumonia). The proposed system validates its dominance over existing methods such as VGG or Densnet, with values exceeding 99.63% in many metrics, such as accuracy, precision, recall, F1-score, and Area under the curve (AUC), based on the performance results obtained

Unsupervised Outlier Detection in IOT Using Deep VAE

The Internet of Things (IoT) refers to a system of interconnected, internet-connected devices and sensors that allows the collection and dissemination of data. The data provided by these sensors may include outliers or exhibit anomalous behavior as a result of attack activities or device failure, for example. However, the majority of existing outlier detection algorithms rely on labeled data, which is frequently hard to obtain in the IoT domain. More crucially, the IoT’s data volume is continually increasing, necessitating the requirement for predicting and identifying the classes of future data. In this study, we propose an unsupervised technique based on a deep Variational Auto-Encoder (VAE) to detect outliers in IoT data by leveraging the characteristic of the reconstruction ability and the low-dimensional representation of the input data’s latent variables of the VAE. First, the input data are standardized. Then, we employ the VAE to find a reconstructed output representation from the low-dimensional representation of the latent variables of the input data. Finally, the reconstruction error between the original observation and the reconstructed one is used as an outlier score. Our model was trained only using normal data with no labels in an unsupervised manner and evaluated using Statlog (Landsat Satellite) dataset. The unsupervised model achieved promising and comparable results with the state-of-the-art outlier detection schemes with a precision of ≈90% and an F1 score of 79%

Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning

An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, generally fatal. Cancerous cells may spread to any body part, where they can be life-threatening. Skin cancer is one of the most common types of cancer, and its frequency is increasing worldwide. The main subtypes of skin cancer are squamous and basal cell carcinomas, and melanoma, which is clinically aggressive and responsible for most deaths. Therefore, skin cancer screening is necessary. One of the best methods to accurately and swiftly identify skin cancer is using deep learning (DL). In this research, the deep learning method convolution neural network (CNN) was used to detect the two primary types of tumors, malignant and benign, using the ISIC2018 dataset. This dataset comprises 3533 skin lesions, including benign, malignant, nonmelanocytic, and melanocytic tumors. Using ESRGAN, the photos were first retouched and improved. The photos were augmented, normalized, and resized during the preprocessing step. Skin lesion photos could be classified using a CNN method based on an aggregate of results obtained after many repetitions. Then, multiple transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were used for fine-tuning. In addition to experimenting with several models (the designed CNN, Resnet50, InceptionV3, and Inception Resnet), this study’s innovation and contribution are the use of ESRGAN as a preprocessing step. Our designed model showed results comparable to the pretrained model. Simulations using the ISIC 2018 skin lesion dataset showed that the suggested strategy was successful. An 83.2% accuracy rate was achieved by the CNN, in comparison to the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models

Current status of TORCH infection Seroprevalence in pregnant women: a cross-sectional study in Al Sharqia Governorate, Egypt

Abstract Background TORCH infections during pregnancy significantly impact neonatal and maternal mortality rates worldwide. This study aimed to gather baseline serological data for pregnant women's immunological status to infection and determine if definite TORCH pathogens (cytomegalovirus, rubella virus, and Herpes simplex virus) were associated with Toxoplasma infection, to improve prenatal care and provide appropriate infection control strategies. Methodology Blood samples were gathered from 210 pregnant women attending Al Zagazig University hospitals from February to May 2023. Samples were examined for specific IgM and IgG antibodies against TORCH pathogens by electrochemiluminescence technique. Results Regarding TORCH infection, 60 (28.6%) cases were seronegative, while 77 (36.7%), 63 (30.0%), 56 (26.7%), and 15 (7.1%) were positive IgG antibodies against Toxoplasma gondii, cytomegalovirus, rubella virus, and Herpes simplex virus, respectively. There was no estimate for IgM for cytomegalovirus, rubella virus, or Herpes simplex virus, indicating that no primary infection had been detected during the pregnancy. There was a statistically significant association between seroprevalence of toxoplasmosis infections (IgM and IgG) and age group ≤ 25 years, which is the most common childbearing age group. Cytomegalovirus seropositivity was found in those beyond 25 years (P-value 0.001). Antibodies to mono-infections were found in 97/210 (46.2%) subjects. It is substantially higher under-25 years age group, 71/97 (73.2%), P-value of 0.001. 45/210 (21.4%) participants had antibodies to two agents, with no significant difference in the age group over 25 years, 26/45 (57.8%). Antibodies to three agents were assessed in eight instances, all under 25 years. Conclusions According to our findings, serological evaluation for the TORCH complex in all pregnant women is recommended to determine infection immunity, current immunization regimens, and infection reactivation. Low TORCH antibodies rates amongst pregnant women in Egypt's Sharqia governorate might be an appropriate starting point for prenatal screening initiatives

Mass spectrometry for screening of metabolic disorders: 9-year biochemical genetics experience

Background/aim Inborn errors of metabolism (IEM) are a group of congenital disorders that result from deficiency of enzymes or transporters involved in different metabolic pathways in the human body. The most severe form of these disorders appears early in the neonatal period; however, most types of IEMs are responsive to treatment if started early enough before the appearance of serious complications. The introduction of mass spectrometric techniques for analysis of metabolites accumulated in IEM facilitates the early diagnosis through enabling analysis of a large number of samples in a short period of time using small sample sizes suitable for patients in the neonatal period. The aim of this study was to find out the prevalence of amino acids, fatty acids, and organic acids disorders, using mass spectrometry among Egyptian children with metabolic disorders who were referred to the Biochemical Genetics Lab, Human Genetics, and Genome Research Institute, National Research Centre, Cairo, Egypt, over a period of 9 years. Patients and methods The present study enrolled 9245 children who visited Biochemical Genetics Department, Human Genetics, and Genome Research Institute, National Research Centre Cairo, Egypt, during the period from 2013 to 2021. All children were subjected to quantitative analysis of amino acids and acylcarnitine profiles in blood, using liquid chromatography/tandem mass spectrometry, whereas qualitative analysis of organic acids was done in urine by gas chromatography/mass spectrometry. Results Of 9245 suspected patients, 552 (5.97%) patients were diagnosed with 13 different types of IEM. A total of 383 (4.1%) patients were diagnosed with aminoacidopathies, 167 (1.8%) patients were diagnosed with organic acidurias, and two (0.02%) patients were diagnosed with fatty acid oxidation disorders. Phenylketonuria is the most prevalent IEM of this study (2%) followed by maple syrup urine disease (0.98%). Conclusion The simultaneous analysis of amino acids and acylcarnitines in dried blood spots with analysis of organic acids in urine using mass spectrometry provides an integrated panel for the early detection of IEMs in early years of life, facilitating prompt provision of treatment and avoiding serious complications that can be fatal

Parasitological, Molecular, and Histopathological Investigation of the Potential Activity of Propolis and Wheat Germ Oil against Acute Toxoplasmosis in Mice

Toxoplasmosis is one of the most common parasitic zoonoses that affects all vertebrates. The drugs most commonly used against toxoplasmosis have many side effects, making the development of new antiparasitic drugs a big challenge. The present study evaluated the therapeutic effectiveness of novel herbal treatments, including propolis and wheat germ oil (WGO), against acute toxoplasmosis. A total of 50 albino mice were divided into five groups: group 1 (G1) (non-infected and non-treated); group 2 (G2) (infected without treatment); group 3 (G3) (treated with propolis); group 4 (G4) (treated with WGO); group 5 (G5) (treated with a combination of propolis and WGO). The effects of the herbal substances on different organs, mainly liver, spleen, and lungs, were investigated using parasitological, molecular, and histopathological examinations. The results of parasitological examination demonstrated statistically significant (p < 0.05) differences in the parasitic load between treated groups (G3, G4, and G5) compared to the control positive group (G2). These differences were represented by a significant reduction in the parasite load in stained tissue smears from the liver obtained from the animals treated with propolis (G3) compared to the parasite load in the positive control group. Similarly, animals (G4) treated with WGO exhibited a significant reduction in the parasite load versus the positive control group, while the lowest parasite load was found in G5, treated with propolis and WGO. Quantification of the parasite burden through molecular methods (PCR) revealed similar findings represented by reduction in the parasite burden in all treated groups with WGO and propolis as compared to the control group. Importantly, these previous parasitological and molecular findings were accompanied by a marked improvement in the histopathological picture of the liver, spleen, and lungs. In conclusion, propolis and WGO showed a good combination of therapeutic efficacy against acute toxoplasmosis