Khalaf Alshamrani

Background: This paper aimed to enhance the diagnostic process of lung abnormalities in computed tomography (CT) images, particularly in distinguishing cancer cells from normal chest tissue. The rapid and uneven growth of cancer cells, presenting with variable symptoms, necessitates an advanced approach for accurate identification. Objective: To develop a dual-sampling network targeting lung infection regions to address the diagnostic challenge. The network was designed to adapt to the uneven distribution of infection areas, which could be predominantly minor or major in different regions. Methods: A total of 150 CT images were analyzed using the dual-sampling network. Two sampling approaches were compared: the proposed dual-sampling technique and a uniform sampling method. Results: The dual-sampling network demonstrated superior performance in detecting lung abnormalities compared to uniform sampling. The uniform sampling method, the network results: an F1-Score of 94.2%, accuracy of 94.5%, sensitivity of 93.5%, specificity of 95.4%, and an area under the curve (AUC) of 98.4%. However, with the proposed dual-sampling method, the network reached an F1-score of 94.9%, accuracy of 95.2%, specificity of 96.1%, sensitivity of 94.2%, and an AUC of 95.5%. Conclusion: This study suggests that the proposed dual-sampling network significantly improves the precision of lung abnormality diagnosis in CT images. This advancement has the potential to aid radiologists in making more accurate diagnoses, ultimately benefiting patient treatment and contributing to better overall population health. The efficiency and effectiveness of the dual-sampling approach in managing the uneven distribution of lung infection areas are key to its success.

Background: Early detection of lung cancer through accurate diagnosis of malignant lung nodules using chest CT scans offers patients the highest chance of successful treatment and survival. Despite advancements in computer vision through deep learning algorithms, the detection of malignant nodules faces significant challenges due to insufficient training datasets. Methods: This study introduces a model based on collaborative deep learning (CDL) to differentiate between cancerous and non-cancerous nodules in chest CT scans with limited available data. The model dissects a nodule into its constituent parts using six characteristics, allowing it to learn detailed features of lung nodules. It utilizes a CDL submodel that incorporates six types of feature patches to fine-tune a network previously trained with ResNet-50. An adaptive weighting method learned through error backpropagation enhances the process of identifying lung nodules, incorporating these CDL submodels for improved accuracy. Results: The CDL model demonstrated a high level of performance in classifying lung nodules, achieving an accuracy of 93.24%. This represents a significant improvement over current state-of-the-art methods, indicating the effectiveness of the proposed approach. Conclusion: The findings suggest that the CDL model, with its unique structure and adaptive weighting method, offers a promising solution to the challenge of accurately detecting malignant lung nodules with limited data. This approach not only improves diagnostic accuracy but also contributes to the early detection and treatment of lung cancer, potentially saving lives.

Introduction: The paper presents a hybrid generative/discriminative classification method aimed at identifying abnormalities, such as cancer, in lung X-ray images. Methods: The proposed method involves a generative model that performs generative embedding in Probabilistic Component Analysis (PrCA). The primary goal of PrCA is to model co-existing information within a probabilistic framework, with the intent to locate the feature vector space for X-ray data based on a defined kernel structure. A kernel-based classifier, grounded in information-theoretic principles, was employed in this study. Results: The performance of the proposed method is evaluated against nearest neighbour (NN) classifiers and support vector machine (SVM) classifiers, which use a diagonal covariance matrix and incorporate normal linear and non-linear kernels, respectively. Discussion: The method is found to achieve superior accuracy, offering a viable solution to the class of problems presented. Accuracy rates achieved by the kernels in the NN and SVM models were 95.02% and 92.45%, respectively, suggesting the method’s competitiveness with state-of-the-art approaches.

Objectives: To measure the cranial capacity of members of the Saudi adult population across ages and genders. Methods: This was a retrospective cross-sectional study that used 488 Computed Tomography (CT) scans of heads (of which 275 males) to measure cranial volume. The CT slices 0.625 mm thick were uploaded using the freely available software “3D-Slicer”, which then reconstructed the images and built a 3D module. Results: The mean (±SD) cranial capacity of the males was 1481.6 (±110) cm³ (range: 1241–1723 cm3), whereas the cranial capacity of the females was 1375.4 (±104) cm³ (range: 1203–1678 cm3). This study showed that the males had a mean cranial capacity that was 7% greater than that of the females in this study. The average cranial capacity of the males between the ages of 31 and 40 years was statistically significantly larger to that of the males aged 61–80 (p<0.05). Conclusion: This study demonstrated that the average cranial capacity of the males was larger than that of the females. These study results can help to determine the normal cranial capacity of adults in Saudi Arabia. Further work should be carried out to aid in establishing reference data for the Saudi population.

BACKGROUND: By providing both functional and anatomical information from a single scan, digital imaging technologies like PET/CT and PET/MRI hybrids are gaining popularity in medical imaging industry. In clinical practice, the median value (SUVmed) receives less attention owing to disagreements surrounding what defines a lesion, but the SUVmax value, which is a semi-quantitative statistic used to analyse PET and PET/CT images, is commonly used to evaluate lesions. OBJECTIVE: This study aims to build an image processing technique with the purpose of automatically detecting and isolating lesions in PET/CT images, as well as measuring and assessing the SUVmed. METHODS: The pictures are separated into their respective lesions using mathematical morphology and the crescent region, which are both part of the image processing method. In this research, a total of 18 different pictures of lesions were evaluated. RESULTS: The findings of the study reveal that the threshold is satisfied by both the SUVmax and the SUVmed for most of the lesion types. However, in six instances, the SUVmax and SUVmed values are found to be in different courts. CONCLUSION: The new information revealed by this study needs to be further investigated to determine if it has any practical value in diagnosing and monitoring lesions. However, results of this study suggest that SUVmed should receive more attention in the evaluation of lesions in PET and CT images.

Health professionals are obliged to provide the patient and/or his or her guardian with information about examinations and treatment. In the case of examinations involving the use of ionizing radiation, informed consent is essential to weigh the associated benefits and risks. This study evaluates the knowledge and attitude of radiographers regarding informed consent in the radiology department. It is a descriptive cross-sectional study using an online questionnaire, divided into three sections and containing 19 questions, detailing the demographics, knowledge, and attitude of radiographers. Participant responses were scored between 1 and 3, then calculated and averaged to determine mean scores. Student’s t-test was used to determine the statistical significance between means. Mean scores for knowledge and attitudes ranged from 11.60 ±1.72 to 18.73 ±1.84. Radiographers with more experience showed a statistically significant difference (17.45 ±1.23) (p < 0.00) with those with less experience (15.34 ±1.52). Twenty-one percent of the respondents did not seek consent from patients prior to examination. This study demonstrated that these professionals are knowledgeable about informed consent; however, more emphasis should be placed on practice by providing training courses and disseminating correct practice.

In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients’ lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image’s background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.

For the precise preoperative evaluation of complex congenital heart diseases (CHDs) with reduced radiation dose exposure, we assessed the diagnostic validity and reliability of low-dose prospective ECG-gated cardiac CT (CCT). Forty-two individuals with complex CHDs who underwent preoperative CCT as part of a prospective study were included. Each CCT image was examined independently by two radiologists. The primary reference for assessing the diagnostic validity of the CCT was the post-operative data. Infants and neonates were the most common age group suffering from complex CHDs. The mean volume of the CT dose index was 1.44 ± 0.47 mGy, the mean value of the dose-length product was 14.13 ± 5.4 mGy*cm, and the mean value of the effective radiation dose was 0.58 ± 0.13 mSv. The sensitivity, specificity, PPV, NPV, and accuracy of the low-dose prospective ECG-gated CCT for identifying complex CHDs were 95.6%, 98%, 97%, 97%, and 97% for reader 1 and 92.6%, 97%, 95.5%, 95.1%, and 95.2% for reader 2, respectively. The overall inter-reader agreement for interpreting the cardiac CCTs was good (κ = 0.74). According to the results of our investigation, low-dose prospective ECG-gated CCT is a useful and trustworthy method for assessing coronary arteries and making a precise preoperative diagnosis of complex CHDs.

Pneumonia is a common lung disease that is the leading cause of death worldwide. It primarily affects children, accounting for 18% of all deaths in children under the age of five, the elderly, and patients with other diseases. There is a variety of imaging diagnosis techniques available today. While many of them are becoming more accurate, chest radiographs are still the most common method for detecting pulmonary infections due to cost and speed. A convolutional neural network (CNN) model has been developed to classify chest X‐rays in JPEG format into normal, bacterial pneumonia, and viral pneumonia. The model was trained using data from an open Kaggle database. The data augmentation technique was used to improve the model’s performance. A web application built with NextJS and hosted on AWS has also been designed. The model that was optimized using the data augmentation technique had slightly better precision than the original model. This model was used to create a web application that can process an image and provide a prediction to the user. A classification model was developed that generates a prediction with 78 percent accuracy. The precision of this calculation could be improved by increasing the epoch, among other subjects. With the help of artificial intelligence, this research study was aimed at demonstrating to the general public that deep‐learning models can be created to assist health professionals in the early detection of pneumonia.

Cephalometry is a medical test that can detect teeth, skeleton, or appearance problems. In this scenario, the patient’s lateral radiograph of the face was utilised to construct a tracing from the tracing of lines on the lateral radiograph of the face of the soft and hard structures (skin and bone, respectively). Certain cephalometric locations and characteristic lines and angles are indicated after the tracing is completed to do the real examination. In this unique study, it is proposed that machine learning models be employed to create cephalometry. These models can recognise cephalometric locations in X-ray images, allowing the study’s computing procedure to be completed faster. To correlate a probability map with an input image, they combine an Autoencoder architecture with convolutional neural networks and Inception layers. These innovative architectures were demonstrated. When many models were compared, it was observed that they all performed admirably in this task.