Role of diet and dietary habits in causing dental caries among adults reporting to a tertiary care hospital in Pakistan; a case-control study.

Objective: To determine the association of diet and dietary practices with dental caries among adults. Design: A case-control study. Setting: Operative Department, Rawal Institute of Health Sciences, Islamabad, Pakistan. Participants: 300 participants of both genders, aged 25-50 years. Interventions: A food frequency questionnaire and a patient proforma were used to determine the frequency and preferences of diet and dietary habits that may be associated with dental caries among adults, respectively. The diet and dietary habits of 150 adults with caries (cases) were compared with those of 150 adults without dental caries (control). An independent sample T-test was applied to determine the difference in mean age. Mann-Whitney and Chi-Square tests were applied to determine the significance of diet and dietary habits respectively. Multivariate logistic regression analysis determined the odd ratio change in significant variables. P-value ≤0.05 was considered significant. Results: Refined sugar (p-value = 0.69), fruit juices (p-value = 0.45), carbonated beverages (p-value = 0.91), duration of consumption of sugary food (p-value = 0.07), and frequency of brushing (p-value = 0.15) were not found to be significantly associated with dental caries in adults. The gender (p-value = 0.02), preferred time for eating sugary foods (p-value <0.001), smoking (p-value <0.001), and tea consumption (p-value = 0.02) were found to be significantly associated with dental caries. Conclusion: Adults who regularly consumed sugar as a snack other than regular mealtimes were more likely to be associated with dental caries. Men, smokers, and adults who frequently took shots of sugar with their tea were more likely to be associated with dental caries.

Soil erosion vulnerability and soil loss estimation for Siran River watershed, Pakistan: an integrated GIS and remote sensing approach.

Soil erosion is a problematic issue with detrimental effects on agriculture and water resources, particularly in countries like Pakistan that heavily rely on farming. The condition of major reservoirs, such as Tarbela, Mangla, and Warsak, is crucial for ensuring an adequate water supply for agriculture in Pakistan. The Kunhar and Siran rivers flow practically parallel, and the environment surrounding both rivers' basins is nearly identical. The Kunhar River is one of KP's dirtiest rivers that carries 0.1 million tons of suspended sediment to the Mangla reservoir. In contrast, the Siran River basin is largely unexplored. Therefore, this study focuses on the Siran River basin in the district of Manshera, Pakistan, aiming to assess annual soil loss and identify erosion-prone regions. Siran River average annual total soil loss million tons/year is 0.154. To achieve this, the researchers integrate Geographical Information System (GIS) and remote sensing (RS) data with the Revised Universal Soil Loss Equation (RUSLE) model. Five key variables, rainfall, land use land cover (LULC), slope, soil types, and crop management, were examined to estimate the soil loss. The findings indicate diverse soil loss causes, and the basin's northern parts experience significant soil erosion. The study estimated that annual soil loss from the Siran River basin is 0.154 million tons with an average rate of 0.871 tons per hectare per year. RUSLE model combined with GIS/RS is an efficient technique for calculating soil loss and identifying erosion-prone areas. Stakeholders such as policymakers, farmers, and conservationists can utilize this information to target efforts and reduce soil loss in specific areas. Overall, the study's results have the potential to advance initiatives aimed at safeguarding the Siran River watershed and its vital resources. Protecting soil resources and ensuring adequate water supplies are crucial for sustainable agriculture and economic development in Pakistan.

Computer-aided anti-cancer drug discovery of EGFR protein based on virtual screening of drug bank, ADMET, docking, DFT and molecular dynamic simulation studies.

Numerous malignancies, including breast cancer, non-small cell lung cancer, and chronic myeloid leukemia, are brought on by aberrant tyrosine kinase signaling. Since the current chemotherapeutic medicines are toxic, there is a great need and demand from cancer patients to find novel chemicals that are toxic-free or have low toxicity and that can kill tumor cells and stop their growth. This work describes the in-silico examination of substances from the drug bank as EGFR inhibitors. Firstly, drug-bank was screened using the pharmacophore technique to select the ligands and Erlotinib (DB00530) was used as matrix compound. The selected ligands were screened using ADMET and the hit compounds were subjected to docking. The lead compound from the docking was subjected to DFT and MD simulation study. Using the pharmacophore technique, 23 compounds were found through virtual drug bank screening. One hit molecule from the ADMET prediction was the subject of docking study. According to the findings, DB03365 molecule fits to the EGFR active site by several hydrogen bonding interactions with amino acids. Furthermore, DFT analysis revealed high reactivity for DB03365 compound in the binding pocket of the target protein, based on ELUMO, EHOMO and band energy gap. Furthermore, MD simulations for 100 ns revealed that the ligand interactions with the residues of EGFR protein were part of the essential residues for structural stability and functionality. However, DB03365 was a promising lead molecule that outperformed the reference compound in terms of performance and in-vitro and in-vivo experiments needs to validate the study.Communicated by Ramaswamy H. Sarma.

Colonic lipoma mimicking malignancy and presenting as an intussusception: A rare case report.

INTRODUCTION AND IMPORTANCE: Lipomas of the gastrointestinal tract are a rare entity compared to the more common tumors of the gut, such as adenomatous polyps and carcinomas. They were first described by Bauer in 1757. Gastrointestinal lipomas can grow in all segments of the gut, with the highest frequency in the colon. In this case report, we present a rare case of gastrointestinal lipoma mimicking colonic malignancy and causing intussusception, necessitating emergent surgery. This paper highlights the potential diagnostic challenges and therapeutic interventions associated with GI lipomas. CASE PRESENTATION: A 28-year-old female presented with symptoms of abdominal pain, weight loss, vomiting, and changes in bowel habits. Initially, she received a misdiagnosis of Irritable Bowel Syndrome. Subsequent investigations indicated the possibility of colonic malignancy. During the intra-operative biopsy, it was ultimately discovered that she had a colonic lipoma. CLINICAL DISCUSSION: CT revealed an abdominal mass and an intussusception, indicating the need for surgical intervention. A laparoscopic procedure was performed to remove the mass, which alleviated the symptoms. Subsequently, a histological examination confirmed the mass to be a lipoma. CONCLUSION: Differentiating a gastrointestinal lipoma from malignancy is crucial, and careful investigation is necessary to determine if a local excision can be performed instead of a wide excision.

Giant adrenal gland pseudo-cyst: a case report with literature review.

Adrenal Gland Cysts are rare among all the pathologic cysts that occur in human beings; the pseudo-cyst variety even rarer. Adrenal pseudo-cysts are asymptomatic, non-functional, small, and incidentally discovered disease entities. Their clinical presentation is usually the result of their mass effects. Thanks to the advanced diagnostic technology, more such cases are being discovered timely and managed surgically, before life-threatening complications occur. Open surgical treatment remains the treatment of choice for giant cysts.

Variation of insulin-related peptides accompanying the differentiation of Aphis gossypii biotypes and their expression profiles.

Insulin signaling plays a critical role in regulating various aspects of insect biology, including development, reproduction, and the formation of wing polyphenism. This leads to differentiation among insect populations at different levels. The insulin family exhibits functional variation, resulting in diverse functional pathways. Aphis gossypii Glover, commonly known as the cotton-melon aphid, is a highly adaptable aphid species that has evolved into multiple biotypes. To understand the genetic structure of the insulin family and its evolutionary diversification and expression patterns in A. gossypii, we conducted studies using genome annotation files and RNA-sequencing data. Consequently, we identified 11 insulin receptor protein (IRP) genes in the genomes of the examined biotypes. Among these, eight AgosIRPs were dispersed across the X chromosome, while two were found in tandem on the A1 chromosome. Notably, AgosIRP2 exhibited alternative splicing, resulting in the formation of two isoforms. The AgosIRP genes displayed a high degree of conservation between Hap1 and Hap3, although some variations were observed between their genomes. For instance, a transposon was present in the coding regions of AgosIRP3 and AgosIRP9 in the Hap3 genome but not in the Hap1 genome. RNA-sequencing data revealed that four AgosIRPs were expressed ubiquitously across different morphs of A. gossypii, while others showed specific expression patterns in adult gynopara and adult males. Furthermore, the expression levels of most AgosIRPs decreased upon treatment with the pesticide acetamiprid. These findings demonstrate the evolutionary diversification of AgosIRPs between the genomes of the two biotypes and provide insights into their expression profiles across different morphs, developmental stages, and biotypes. Overall, this study contributes valuable information for investigating aphid genome evolution and the functions of insulin receptor proteins.

Primary mycetoma of the calcaneum: Case report on an unusual hazard of barefoot walking.

INTRODUCTION: Mycetoma is a rare tropical fungal infection characterized by a clinical triad of subcutaneous swelling, multiple discharging sinuses, and a purulent discharge containing granules. If left untreated, the disease can progress from cutaneous to intraosseous and can cause osteomyelitis. In very rare instances labeled "primary mycetoma", the fungus is insidiously inoculated directly into the bone and causes osteomyelitis without any preceding cutaneous involvement. This can make the diagnosis very difficult. PRESENTATION OF CASE: A twelve-year-old girl with a history of walking barefoot, presented with pain and inability to bear weight on her left foot. There was no overlying cutaneous involvement. X-ray showed an osteolytic lesion in the calcaneum. After the failure of antibiotic treatment, the diseased bone was excised. Black granules were discovered inside the lesion and their histopathology confirmed a diagnosis of primary eumycetoma. After some time, the disease relapsed, necessitating another debridement. This occurred many times with worsened severity in each successive episode. Because of worsening disease and failure of both antifungal and surgical treatment, foot amputation was done. DISCUSSION: Primary mycetoma is an insidious fungal infection that causes osteomyelitis without any cutaneous findings. Timely diagnosis and treatment provide the best chance of preventing an amputation. CONCLUSION: A high index of suspicion must be maintained for patients presenting with symptoms of osteomyelitis without any skin involvement so that timely diagnosis and treatment can prevent the progression of the disease and the need for amputation.

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing.

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

A Prospective Evaluation of the Association of Ureteral Wall Thickness With Intraoperative Stone Impaction in Ureteroscopy.

Background and objective In this study, we aimed to analyze the association of ureteral wall thickness (UWT) measured on non-contrast CT (NCCT) with stone impaction as found in ureteroscopy (URS). Materials and methods We analyzed 43 patients who underwent URS and pneumatic/laser lithotripsy for ureteric stones from May to November 2022. The UWT was measured by an experienced radiologist on NCCT. Clinical predictors of the impacted stone were calculated by univariate and multivariate regression analysis. The receiver operating characteristic (ROC) curve was calculated for the UWT cutoff to apply it for impaction with different parameters. We also evaluated the association of intra- and postoperative parameters of the two groups with UWT. Results Out of the 43 patients with stones, 26 (60.46%) patients had impacted stones. Univariate analysis was used to analyze the site (left-sided stone impacted more commonly), stone size, stone density [Hounsfield unit (HU)], hydronephrosis, UWT, and duration between initial presentation and surgery, and multivariate analysis was utilized to assess stone density, as well as UWT's association with impacted stones. The ROC curve showed a cutoff of 3.5 mm for UWT with an accuracy of 0.83. High UWT (≥3.5 mm) was associated with a significantly lower stone-free rate, more complications, and mean operative time as compared to low UWT (<3.5 mm) (p<0.05). Conclusion Based on our findings, high UWT is associated with high rates of impacted stones and a lower stone-free rate when compared to low UWT.

Suitability changes of Citrus medica L. var. sarcodactylis Swingle, a medicine-food plants affected by climate warming using the optimized MaxEnt model.

Climatic variables are important conditions for plant growth, development and reproduction. Citrus medica L. var. sarcodactylis Swingle (Rutaceae: Citrus) is one of the traditional bulk Chinese medicinal materials in China with the effects of bacteriostasis, anti-inflammatory, anti-oxidation, anti-cancer cells, regulating the immun. Analyzing the impact of climate change on geographical distribution of C. medica L. var. sarcodactylis can provide strong support for its production layout and agricultural zoning. In our paper, MaxEnt and ArcGIS were applied to simulate the suitable areas of C. medica L. var. sarcodactylis in China from the perspectives of bioclimate, soil, topographic factors and human activities, and the future climate scenarios generated by global climate models (GCMs) were selected to predict its suitable areas in 2050s and 2090s. Results showed that, 1) Under current climate condition, areas of the total, most, moderately and poorly suitable habitats of C. medica L. var. sarcodactylis in China were 177.36×104 km2, 22.27×104 km2, 51.96×104 km2 and 103.13×104 km2 respectively. The range of the most suitable habitat was the narrowest, which was located in the middle east of Sichuan, western Chongqing in the upstream of the Yangtze River Basin, southern Guizhou and western Guangxi in the upstream of the Pearl River Basin, central and southern Yunnan and Southeast Tibet in the Middle-Lower reaches of the Southwest River Basin and western Taiwan. 2) Under the future climate change scenarios, the total suitable area showed a significant increase trend in 2090s, and the change of most, moderately and poorly suitable habitats showed no obvious law. 3) Under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, the centroid of the most suitable habitat of C. medica L. var. sarcodactylis would move to the northwest, southeast and southwest respectively.

Economic burden of tobacco use in Pakistan.

BACKGROUND: The lack of reliable tobacco healthcare and economic cost estimates leaves the tobacco industry undertaxed and thriving in Pakistan and makes the country as one of the top tobacco-consuming nations. To facilitate effective tobacco tax policymaking, this study estimates the economic cost of smoking-attributable diseases and deaths in Pakistan. METHODS: A nationally representative sample survey of 13 000 households was administered to gather the data required to estimate different cost components of smoking-attributable diseases through the prevalence-based approach. FINDINGS: The total smoking-attributable economic cost of all diseases and deaths in Pakistan in the year 2018-2019 for persons aged 35 years or older is 615.07 billion ($3.85 billion). Similarly, three major diseases, namely cancer, cardiovascular disease and respiratory disease, along with associated deaths, cost the nation PKR437.8 billion ($2.7 billion) of which 77% is the indirect cost. The three major diseases make 71% of the total estimated cost, nearly two-thirds of which is borne by rural residents, nine-tenth by males and more than four-fifths by the citizens in the 35-64 years age group. CONCLUSION: The total annual economic costs of all smoking-attributable diseases and deaths and those of the three major diseases equal 1.6% and 1.15% of Pakistan's gross domestic product, respectively. The tax contribution of tobacco sector is merely 20% of the total estimated cost. The finding of huge economic and health costs of smoking makes a convincing case for policymakers to realise the true value of the industry's contribution and raise tobacco taxes to the level of full cost recovery.

Analysis of Nonlinear Convection-Radiation in Chemically Reactive Oldroyd-B Nanoliquid Configured by a Stretching Surface with Robin Conditions: Applications in Nano-Coating Manufacturing.

Motivated by emerging high-temperature manufacturing processes deploying nano-polymeric coatings, the present study investigates nonlinear thermally radiative Oldroyd-B viscoelastic nanoliquid stagnant-point flow from a heated vertical stretching permeable surface. Robin (mixed derivative) conditions were utilized in order to better represent coating fabrication conditions. The nanoliquid analysis was based on Buongiorno's two-component model, which features Brownian movement and thermophoretic attributes. Nonlinear buoyancy force and thermal radiation formulations are included. Chemical reactions (constructive and destructive) were also considered since coating synthesis often features reactive transport phenomena. An ordinary differential equation model was derived from the primitive partial differential boundary value problem using a similarity approach. The analytical solutions were achieved by employing a homotopy analysis scheme. The influence of the emerging dimensionless quantities on the transport characteristics was comprehensively explained using appropriate data. The obtained analytical outcomes were compared with the literature and good correlation was achieved. The computations show that the velocity profile was diminished with an increasing relaxation parameter, whereas it was enhanced when the retardation parameter was increased. A larger thermophoresis parameter induces an increase in temperature and concentration. The heat and mass transfer rates at the wall were increased with incremental increases in the temperature ratio and first order chemical reaction parameters, whereas contrary effects were observed for larger thermophoresis, fluid relaxation and Brownian motion parameters. The simulations can be applied to the stagnated nano-polymeric coating of micromachines, robotic components and sensors.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Virtual Reality as a Surgical Care Package for Patients Undergoing Weight Loss Surgery: A Narrative Review of the Impact of an Emerging Technology.

While bariatric surgery is regarded as the most effective treatment for people with severe and morbid obesity, its pathway is regarded as a complex one due to the multidisciplinary approaches required from pre-surgery education until long-term management. This is essential to maintain weight loss and improve the quality of life after bariatric surgery. Although these approaches are broadened, patient education, pre-operative preparation, behavioural therapy, rehabilitation, and dietary changes are regarded as the main domains in such complex care. With the increase in technological adaptation in medical services, virtual reality (VR) has shown many benefits that can be utilized in the care of bariatric patients undergoing surgery. However, VR has not been innovated to be a multidomain care package in which bariatric patients could benefit throughout their journey from the pre-operative optimization, recovery, and long-term follow-up. This review aims to give a brief description of some of the applications of VR technology and question whether it has the potential to be considered as a virtual ecosystem to improve the bariatric patients' experience and pathway throughout surgery and follow-up.

Spontaneous Isolated Thrombosed True Popliteal Aneurysm in an Eight-Year-Old Child: A Rare Case Report With Literature Review.

Spontaneous aneurysms are rare in the pediatric age group. Aneurysms of peripheral arteries are even rarer. The diagnosis should not be missed to prevent distal limb ischemia and life-threatening complications. Hence, timely surgery to save the affected limb is advised. There is an increasing number of reported cases of such aneurysms in the English scientific literature. We present a rare case of pediatric idiopathic popliteal artery aneurysm (PAA), with no known risk factors. This scientific writing is unique in its way of reporting an idiopathic aneurysm with spontaneous onset. However, we have successfully investigated and managed the patient considering the established guidelines on aneurysmal surgery.

IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing.

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.

Supervised Machine Learning Empowered Multifactorial Genetic Inheritance Disorder Prediction.

Fatal diseases like cancer, dementia, and diabetes are very dangerous. This leads to fear of death if these are not diagnosed at early stages. Computer science uses biomedical studies to diagnose cancer, dementia, and diabetes. With the advancement of machine learning, there are various techniques which are accessible to predict and prognosis these diseases based on different datasets. These datasets varied (image datasets and CSV datasets) around the world. So, there is a need for some machine learning classifiers to predict cancer, dementia, and diabetes in a human. In this paper, we used a multifactorial genetic inheritance disorder dataset to predict cancer, dementia, and diabetes. Several studies used different machine learning classifiers to predict cancer, dementia, and diabetes separately with the help of different types of datasets. So, in this paper, multiclass classification proposed methodology used support vector machine (SVM) and K-nearest neighbor (KNN) machine learning techniques to predict three diseases and compared these techniques based on accuracy. Simulation results have shown that the proposed model of SVM and KNN for prediction of dementia, cancer, and diabetes from multifactorial genetic inheritance disorder achieved 92.8% and 92.5%, 92.8% and 91.2% accuracy during training and testing, respectively. So, it is observed that proposed SVM-based dementia, cancer, and diabetes from multifactorial genetic inheritance disorder prediction (MGIDP) give attractive results as compared with the proposed model of KNN. The application of the proposed model helps to prognosis and prediction of cancer, dementia, and diabetes before time and plays a vital role to minimize the death ratio around the world.

Breast Cancer Prediction Empowered with Fine-Tuning.

In the world, in the past recent five years, breast cancer is diagnosed about 7.8 million women's and making it the most widespread cancer, and it is the second major reason for women's death. So, early prevention and diagnosis systems of breast cancer could be more helpful and significant. Neural networks can extract multiple features automatically and perform predictions on breast cancer. There is a need for several labeled images to train neural networks which is a nonconventional method for some types of data images such as breast magnetic resonance imaging (MRI) images. So, there is only one significant solution for this query is to apply fine-tuning in the neural network. In this paper, we proposed a fine-tuning model using AlexNet in the neural network to extract features from breast cancer images for training purposes. So, in the proposed model, we updated the first and last three layers of AlexNet to detect the normal and abnormal regions of breast cancer. The proposed model is more efficient and significant because, during the training and testing process, the proposed model achieves higher accuracy 98.44% and 98.1% of training and testing, respectively. So, this study shows that the use of fine-tuning in the neural network can detect breast cancer using MRI images and train a neural network classifier by feature extraction using the proposed model is faster and more efficient.

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.