315 research outputs found
X-ray image analysis for dental disease: A deep learning approach using EfficientNets
No full textDental cavities are a highly common persistent dental problem that impacts populations across different age groups on a global scale. It is crucial to get a dental issue diagnosed as early as possible and with as much accuracy as possible to treat it efficiently and prevent any related issues. If a dental infection is not treated, it will eventually grow and cause tooth loss. Dental X-ray images are crucial and beneficial in the diagnostic process of dental diseases for dentists. By applying Deep Learning (DL) techniques to dental X-ray images, dental experts can efficiently and precisely etect dental conditions, including dental cavities, fillings and implants. The objective of this research is to assess the performance of DL-based methods for dental disease detection via panoramic radiographs. In this study, we evaluated the performance of all of the EfficientNet variants (e.g., EfficientNets B0-B7) to determine which one is the most effective model for detecting dental disease. Moreover, we utilized the Borderline Synthetic Minority Oversampling Technique (SMOTE) to cope with the issue related to the minority classes contained in the dataset. To assess the efficacy of the model, various metrics are employed, including recall, accuracy, precision, loss, and F1-score. As a result, the performance of the EfficientNet-B5 model was superior to that of the other EfficientNet models. The EfficientNet-B5 model achieved the following values for its metrics: F1-score, accuracy, recall, AUC, and precision: 98.37%, 98.32%, 98.32%, 99.21%, and 98.32%, respectively. The accuracy rates for the EfficientNet-B0, EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B6, and EfficientNet-B7, are 91.59%, 94.12%, 93.28%, 85.71%, 94.96%, 96.64% and 90.76%, respectively. The results indicated that the EfficientNet-B5 model performs better than other EfficientNet classifiers, which supports dental professionals significantly in the recognition of dental diseases
sj-docx-1-onc-10.1177_11795549221084832 – Supplemental material for Body Mass Index and Diabetes Mellitus May Predict Poorer Overall Survival of Oral Squamous Cell Carcinoma Patients: A Retrospective Cohort From a Tertiary-Care Centre of a Resource-Limited Country
No full textSupplemental material, sj-docx-1-onc-10.1177_11795549221084832 for Body Mass Index and Diabetes Mellitus May Predict Poorer Overall Survival of Oral Squamous Cell Carcinoma Patients: A Retrospective Cohort From a Tertiary-Care Centre of a Resource-Limited Country by Yumna Adnan, Syed Muhammad Adnan Ali, Muhammad Sohail Awan, Nida Zahid, Muhammad Ozair Awan, Hammad Afzal Kayani and Hasnain Ahmed Farooqui in Clinical Medicine Insights: Oncology</p
IDD-Net: A Deep Learning Approach for Early Detection of Dental Diseases Using X-Ray Imaging
No full textEarly detection of dental diseases such as cavities, periodontitis, and periapical infections is crucial for effective management and prevention, as these conditions can lead to severe complications if left untreated. However, traditional diagnostic methods are often manual, time consuming, and heavily reliant on expert judgment, which can introduce variability and delay in diagnosis. To address these critical challenges, we propose IDD-Net (Identification of Dental Disease Network), a novel deep learning-based model designed for the automatic detection of dental diseases using panoramic X-ray images. The proposed framework leverages Convolutional Neural Networks (CNN) to enhance the accuracy and efficiency of dental condition classification, thereby significantly improving the diagnostic process. In our comprehensive evaluation, IDD-Net’s performance is rigorously compared to four state-of-the-art deep learning models: AlexNet, InceptionResNet-V2, Xception, and MobileNet-V2. To tackle the issue of class imbalance, we employ the Synthetic Minority Over-sampling Technique with Tomek links (SMOTE Tomek), ensuring a balanced sample distribution that enhances model training. Experimental results showcase IDDNet’s exceptional performance, achieving a 99.97% AUC, 98.99% accuracy, 98.24% recall, 98.99% precision, and a 98.97% F1-score, thus outperforming benchmark classifiers. These findings underscore the transformative potential of IDD-Net as a reliable and efficient tool for assisting dental and medical professionals in the early detection of dental diseases. By streamlining the diagnostic process, IDD-Net not only improves patient outcomes but also has the potential to reshape standard practices in dental care, paving the way for more proactive and preventive
approaches in oral health management
PDDNet: Deep Learning Based Dental Disease Classification through Panoramic Radiograph Images
No full textThe high prevalence of dental cavities is a global public health concern. If untreated, cavities can lead to tooth loss, but timely detection and treatment can prevent this outcome. X-ray imaging provides crucial insights into the structure of teeth and surrounding tissues, enabling dentists to identify issues that may not be immediately visible. However, manual assessment of dental X-rays is time-consuming and prone to errors due to variations in dental structures and limited expertise. Automated analysis technology can reduce dentists’ workload and improve diagnostic accuracy. This study proposes the Prediction of Dental Disease Network (PDDNet), a CNN-based model for classifying three categories of dental disease: cavities, fillings, and implants, using X-ray images. PDDNet’s performance is compared with six well-known deep CNN classifiers: DenseNet-201, Xception, ResNet50V2, Inception-V3, Vgg-19, and EfficientNet-B0. To ensure balanced class distribution and enhance classification accuracy, the ADASYN oversampling technique is employed. PDDNet achieves an impressive accuracy of 99.19%, recall of 99.19%, precision of 99.19%, AUC of 99.97%, and F1-score of 99.17%, outperforming the other classifiers across multiple performance metrics. These findings demonstrate PDDNet’s potential to provide significant assistance to dental professionals in diagnosing dental diseases
sj-jpg-1-ijd-10.1177_10567895221135054 - Supplemental material for Investigation into a comprehensive forming limit curve considering multi-parameters effects for semi-cured FMLs through uniform pressure loading test
No full textSupplemental material, sj-jpg-1-ijd-10.1177_10567895221135054 for Investigation into a comprehensive forming limit curve considering multi-parameters effects for semi-cured FMLs through uniform pressure loading test by Meng Zhang, Hasnain Ali Mirza, Ye Chi Zhang, Muhammad Nazim Tabasum, HL Lang and Ping Zheng Zou in International Journal of Damage Mechanics</p
STRESS AS A RISK FACTOR OF ACID PEPTIC DISEASE
No full textDr. Syed Hasnain Ali*, Dr. Muhammad Haleem and Dr. Muhammad Salman Abi
THE STUDY OF COMPARISON OF DIFFERENT CHELATION TREATMENTS FOR THE PATIENTS OF THALASSEMIA
No full text*Rida Ali, Arooj Saqib Malik and Muhammad Hasnain Bashi
THE STUDY OF COMPARISON OF NIFEDIPINE AND NITROGLYCERINE USE IN PATIENTS OF PRETERM LABOUR
No full text*Rida Ali, Arooj Saqib Malik and Muhammad Hasnain Bashi
Study of Pakistan pilot project farmer-leaders to Nepal
No full textFarmer participation / Irrigation management / Farmer managed irrigation systems / Irrigated farming / Sustainable agriculture / Institution building / Pakistan
اردو میں منظوم تراجم کی روایت
No full textTranslation has become as one of the most important and fundamental needs of modern era. Translation is the art which not only helps us to get an opportunity of benefiting from the academic and literary treasure of other countries but opens an avenue of unlimited growth for one language as well. Humanity has utilized the art of translation in order to meet its religious, social and financial needs in each and every era.The scope of Urdu language is enriched with different translations which certainly begins with the translations of Holy Quran. After that the process kept on enriching other fields of knowledge and literature. During the course of translation master pieces of prose and poetry of other languages were transformed into Urdu. Ali Garh College, Dehli College and Dar-ul-Tarjuma Usmania contributed well in this regard. "Makhzan" has also played vital role as far as the poetical translations are concerned. The poetical Urdu translations are indebted and obliged by the great work of Allama Muhammad Iqbal, Muhammad Hussain Azad, Ameer Chand Bahar, Syed Shakir Ali and Syed Ali Hasnain Naqvi. This tradition of poetical Urdu translations from classical and modern western poetry collection is still flourishing and progressing with full pace and in desired direction. This tradition/process is helping the quantity of Urdu poetry and more importantly this art is heading towards the desired destination of maturity and growth.
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