Diagnosis Of Oral Cancer Research Articles

Unlocking the Therapeutic Potential of Oral Cancer Stem Cell-Derived Exosomes.

Oral cancer (OC) presents a significant global health burden with rising incidence rates. Despite advancements in diagnosis and treatments, the survival rate for OC patients, particularly those with advanced or recurrent disease, remains low at approximately 20%. This poor prognosis is often due to a small population of cancer stem cells (CSCs) that are capable of self-renewal and immune evasion, playing pivotal roles in proliferation, tumor initiation, progression, metastasis, and therapy resistance. Exosomes, which are nano-sized extracellular vesicles (EVs), have emerged as crucial mediators of cell-to-cell communication within the tumor microenvironment (TME). These vesicles carry diverse molecules such as DNA, RNA, proteins, lipids, and metabolites, influencing various cellular processes. Emerging evidence suggests that CSC-derived EVs significantly promote tumor progression and metastasis and maintain the balance between CSCs and non-CSCs, which is vital for intracellular communication within the TME of oral cancer. Recent reports indicate that oral cancer stem cell-derived EVs (OCSC-EVs) influence stemness, immune evasion, metastasis, angiogenesis, tumor reoccurrence, and drug resistance. Understanding OCSC-EVs could significantly improve oral cancer diagnosis, prognosis, and therapy. In this mini-review, we explore OCSC-derived exosomes in oral cancer, examining their potential as diagnostic and prognostic biomarkers that reflect CSC characteristics, and delve into their therapeutic implications, emphasizing their roles in tumor progression and therapy resistance. However, despite their promising potential, several challenges remain, including the need to standardize isolation and characterization methods and to elucidate exosome-mediated mechanisms. Thus, a comprehensive understanding of OCSC-EVs could pave the way for innovative therapeutic strategies that have the potential to improve clinical outcomes for OC patients.

Significance of Saliva as a Sensor Detector in Marking Oral Cancer

One of the most deadly forms of cancer is oral cancer. Since the screening methods currently in use are poor, a consistent portion of oral health issues are diagnosed at any late stage. Because existing diagnostic processes are complex and imprecise, it would be imperative to enhance them. The possibility of diagnosis by the discovery of biomarkers from biological fluids. The salivary glands secrete protein, peptides, electrolytes, organic and inorganic salts, and gingival crevicular fluids and mucosal transudates, which contribute in a complementary manner to human whole mouth saliva. This study focuses on biosensors that work at the molecular level, such as DNA, RNA, and protein biosensors, and it discusses tactics that use diverse biosensors to target different types of biomarkers. We also looked at non-invasive electrochemical techniques, optical techniques, and nanobiosensors for analyzing the cancer biomarkers found in bodily fluids including serum and saliva. Consequently, this review clarifies the advancements made in the creation of novel biosensors for the early identification and diagnosis of oral cancer. To clearly clarify the clinical situation, an introduction to oral cancer diagnosis, prognosis, and treatment is provided. Saliva is then offered as an alternative biofluid, along with its benefits, drawbacks, and methods of collection. It is suggested that salivary biomarkers be used as non-invasive diagnostic instruments.

Evaluation of the expression of miRNA-214 and circ-0005407 markers and their associated ZFAND3 gene in Oral squamous cell carcinoma

Background and objectiveCancer biomarkers serve as measurable indicators for the early detection of cancer, as they can be detected in sample tissues. They play a crucial role in optimizing decision-making in clinical practice. To advanve in this field, it is necessary to identify of markers with higher sensitivity, specificity, and positive predictive value. Therfore, wer have chosen to investigate the expression of multiple biomarkers simultaneously in oral squamous cell carcinoma. Materials and methodsIn this experimental study, we investigated the expression levels of miRNA-214, circ-0005407, and ZFAND3 genes using the quantitative RealTime PCR method. The samples examined included 30 samples of fresh cancerous tissue and adjacent normal tissue obtained from the Cancer Institute of Imam Khomeini Hospital. The data were analyzed using the dependent samples t-test and ANOVA test. FindingsThe mean CT values of circ-0005407, miRNA-214, and ZFAND3 in the cancerous and normal groups were (15.48 ± 1.62 & 13.23 ± 1.63), (16.64 ± 1.51 & 13.21 ± 1.29), and (14.41 ± 1.07 & 16.72 ± 1.28), respectively. There was a statistically significant difference in the expression levels of all markers in the two investigated groups (P˂0.05). The fold change levels in circ-0005407, miRNA-214, and ZFAND3 gene markers were (0.51,0.22,6.34) respectively, indicating an increase in gene expression in cancerous tissue compared to normal. However, the other two markers showed decreased expression in cancerous tissue compared to normal tissue. ConclusionBased on the data obtained regarding the change in expression levels of these markers in cancerous tissue compared to normal tissue, it seems that these biomarkers can be used in the early diagnosis of oral cancer.

A comprehensive dataset of annotated oral cavity images for diagnosis of oral cancer and oral potentially malignant disorders

ObjectivesThis study aims to address the critical gap of unavailability of publicly accessible oral cavity image datasets for developing machine learning (ML) and artificial intelligence (AI) technologies for the diagnosis and prognosis of oral cancer (OCA) and oral potentially malignant disorders (OPMD), with a particular focus on the high prevalence and delayed diagnosis in Asia. Materials and methodsFollowing ethical approval and informed written consent, images of the oral cavity were obtained from mobile phone cameras and clinical data was extracted from hospital records from patients attending to the Dental Teaching Hospital, Peradeniya, Sri Lanka. After data management and hosting, image categorization and annotations were done by clinicians using a custom-made software tool developed by the research team. ResultsA dataset comprising 3000 high-quality, anonymized images obtained from 714 patients were classified into four distinct categories: healthy, benign, OPMD, and OCA. Images were annotated with polygonal shaped oral cavity and lesion boundaries. Each image is accompanied by patient metadata, including age, sex, diagnosis, and risk factor profiles such as smoking, alcohol, and betel chewing habits. ConclusionResearchers can utilize the annotated images in the COCO format, along with the patients’ metadata, to enhance ML and AI algorithm development.

Histopathological Features of Oral Cancer: Diagnostic and Prognostic Indicators

Background: Oral cancer, mostly oral squamous cell carcinoma (OSCC) was shown to be one of the principal causes leading to a great health burden worldwide with significant morbidity and mortality Objective: The objective of our research was to build an archetypal portrait of histopathological scenario in oral cancer and the clinical importance that can be leveraged as efficacious diagnostic markers for conducting precision diagnostics. Study Design: A Retrospective Study. Duration and Place of the Study: This study was conducted at Department Watim medical and dental college, Rawat, between 3rd February 2023 to 2nd January 2024. Material and Methods: This research comprised of 200 patients having oral squamous cell carcinoma (OSCC) and other malignancies in oral region. Criteria for inclusion: patients having a histologically confirmed diagnosis of oral cancer with complete clinical records and enough biopsy tissue samples. The exclusion criteria were recurrent tumors, previous chemotherapy or radiotherapy and incomplete histopathological records. Results: The study population was the 200 patients diagnosed with oral cancer. The mean age of our patients at the time of diagnosis was 58.4 ± 12.3. The age range was as follows; 16.0% of the patients were under 40, 53.5% patients were 40-60, 30.5% patients wer over 60. Well-differentiated tumors had a baseline HR 1.00, whereas the HR in moderately differentiated cases was 1.45 (95% CI: 1.10-1.90, p=0.02) and in poorly differentiated 2.10 (95% CI: 1.50-2.90, p<0.01). Conclusion: Our study in oral cancer supplied a comprehensive assessment on the histopathological and IHC characteristic of it for revealing their diagnostic, and prognostic functions. Keywords: Histopathology, Oral Cancer, Diagnostic Indicators, Prognostic Markers.

Diagnosis of oral cancer using deep learning algorithms

The aim of this study was to use deep learning for the automatic diagnosis of oral cancer, employing images of the lips, mucosa, and oral cavity. A deep convolutional neural network (CNN) model, augmented with data, was proposed to enhance oral cancer diagnosis. We developed a Mobile Net deep CNN designed to detect and classify oral cancer in the lip, mucosa, and oral cavity areas. The dataset comprised 131 images, including 87 positive and 44 negative cases. Additionally, we expanded the dataset by varying cropping, focus, rotation, brightness, and flipping. The diagnostic performance of the proposed CNN was evaluated by calculating accuracy, precision, recall, F1 score, and area under the curve (AUC) for oral cancer. The CNN achieved an overall diagnostic accuracy of 90.9% and an AUC of 0.91 with the dataset for oral cancer. Despite the limited number of images of lips, mucosa, and oral cavity, the CNN method developed for the automatic diagnosis of oral cancer demonstrated high accuracy, precision, recall, F1 score, and AUC when augmented with data.

Oral cancer diagnosis communication: Impact of an educational intervention using the SPIKES protocol.

This study evaluated the influence of a single educational intervention on the perception and knowledge of strategies for communicating oral cancer diagnoses. A educational intervention, 72 dentists and 41 dental undergraduates participated in the 'Maio Vermelho Project', a continuing education activity. Participants completed a 14-question online questionnaire concerning their experiences and perceptions of delivering difficult news. The educational intervention featured an interview illustrating the SPIKES protocol, broadcast on YouTube. Participants had a mean age of 40 years. A minority (21.2%) had encountered or experienced communicating an oral cancer diagnosis. Exposure to lectures on this topic during their education was uncommon (22.1%) but more prevalent among students. After the intervention, confidence in communicating a cancer diagnosis (29.2%) and addressing the patient's family (30.1%) in line with the SPIKES protocol increased. A training deficit persists in delivering cancer diagnoses, highlighting the need for educational interventions to empower students and professionals in this critical procedure. Integration of this topic into the dental undergraduate curriculum is imperative. Effectively communicating a cancer diagnosis poses challenges to healthcare professionals, impacting treatment outcomes. Implementing educational interventions ensures that professionals are well prepared to navigate this complex task, ultimately improving patient care.

Epidemiological Study of Oral Cancer Cases Over a 10-Year Period.

Because it causes high rates of morbidity and death, oral cancer represents a major global public health concern. It is vital to comprehend the epidemiological trends of oral cancer instances to execute efficacious preventive and therapy tactics. The objective of this retrospective study was to examine the epidemiological patterns of cases of oral cancer in a tertiary care environment over a period of 10 years. Retrospective analysis was done on patient records of those who received an oral cancer diagnosis at a tertiary care facility. We collected and evaluated data on treatment methods, clinical factors, and demographics. The study included 300 instances of oral cancer in total. The patients' average age was 58.4 years, and 66.7% of them were men. The majority of patients (66.6%) had advanced-stage cancer when they first appeared, with the tongue being the most common original tumor location (40.0%). In summary, this study sheds important light on the epidemiology of oral cancer patients during a 10-year period in a tertiary care context. The results highlight the significance of focused interventions for oral cancer early detection and prevention as well as the requirement for better access to healthcare services for prompt diagnosis and treatment.

Socioeconomic Status, Length of Stay, and Postoperative Complications in Oral Cavity Squamous Cell Carcinoma.

Background: Despite universal healthcare in Canada, low socioeconomic status (SES) has been associated with worse survival in oral cavity squamous cell carcinoma (OCSCC) patients. However, the relationship between SES and outcomes during the acute postoperative period is poorly defined. Hamilton, Ontario, presents a unique population with widely varying SES within the same geography. The objective of this study was to examine the relationship between SES, length of hospital stay (LOHS), and postoperative complications in OCSCC. Methods: Newly diagnosed OCSCC patients receiving primary surgical treatment from 2010 to 2014 were identified within a prospectively collected database. Inclusion criteria included age >18 years old, pathological diagnosis of oral cavity cancer, and primary surgical treatment with curative intent. Patients were excluded if they were undergoing palliative treatment or had previous head and neck surgery/radiotherapy. Postal codes were used to identify neighborhood-level socioeconomic variables via 2011 Canada Census data. Income quartiles were defined from groups of neighboring municipalities based on Canada Census definitions. Demographic, social, pathological, staging, and treatment data were collected through chart review. Results: One hundred and seventy-four patients were included in the final analysis. OCSCC patients with lower SES were more likely to be younger (P = .041), male (P = .040), have significant tobacco and alcohol use (P = .001), higher Charlson Comorbidity Index (CCI; P = .014), lower levels of education (P = .001), and have lower employment levels (P = .001). Lower SES patients had higher clinical tumor (P = .006) and clinical nodal (P = .004) staging and were more likely to receive adjuvant therapy (P = .001) and G-tubes (P = .001). Multivariable regression analysis showed that low SES was a statistically significant predictor of postoperative complications [β 2.50 (95% confidence interval (CI) 0.200, 3.17); P = .014] and LOHS [β 2.03 (95% CI 1.06, 2.99); P = .0001]. Tobacco and alcohol use, clinical tumor, and nodal stage, CCI, and planned adjuvant therapy were also statistically significant predictors of postoperative complications and LOHS (P < .05). Conclusion: Patients with lower SES have more advanced OCSCC disease with increased comorbidities that owes itself to more acute postoperative complications and LOHS within this study population. Patients with low SES should be identified as patients that require more support during their cancer treatment.

Assessing the information provided regarding oral cancer by dental practice websites within the Greater Manchester area.

With increasing numbers of oral cancer diagnoses and guidance recommending that patients approach their doctors or dentists for assessments, when searching for information regarding mouth cancer, patients may first look to their dental practice website. This study aimed to evaluate the variance of patient information provided regarding oral cancer on dental practice websites. Dental practices within the Manchester area with an active website were included with assessment of whether there was any information provided regarding reducing oral cancer risk factors, signs to look out for and if oral cancer screening was included as part of the general check-up, using a three-point score based on the joint statement from the British and Irish Society for Oral Medicine (BISOM) and Cancer Research UK (CRUK) regarding oral cancer diagnosis and prevention. In total, 66.6% (n=60) of practices provided no accessible information regarding oral cancer screening and prevention on their websites. Only 12.2% (n=11) discussed all three factors of oral screening, symptoms and reducing risk factors. Screening as part of routine examination was most frequently mentioned at 27% (n=24), risk factors were discussed by 23% (n=21) of practices and symptoms to be aware of by 13% (n=12). There is a lack of information available to patients on dental practice websites regarding oral cancer. To overcome lack of quality assurance on the internet, dentists can provide factual information via their dental practice websites. This could help with improving patient awareness and therefore aiding in early detection, improving patient outcomes.

Protocol for correlation of histological risk assessment/scoring system with a depth of invasion in oral squamous cell carcinoma.

The commonest type of cancer in the head and neck region is oral squamous cell carcinoma (OSCC) due to its high rates of occurrence and mortality. The early diagnosis of oral cancer gives better prognosis. Brandwein-Gensler criteria predict the early stage of OSCC cases with a high risk of locoregional recurrence. To correlate Brandwein-Gensler criteria and depth of invasion of OSCC with three-year survival. In the study, This study will include 80 random histopathologically-diagnosed cases of OSCC. hematoxylin-eosin (HE)-stained section slides will be used to evaluate, Brandwein and Gensler criteria by three histopathologists in a blinded manner. The depth of invasion assessment will be done from the basement-membrane (BM), in regions where the BM has been lost, as well as from an illustrative line connecting the BM from the neighbouring epithelium to the point of deepest tumour invasion in the connective-tissue stroma with the help of a research microscope (Leica-DMLB2) in resected tissue specimens of OSCC cases. The present study will find the correlation between Brandwein-Gensler criteria and depth of invasion in OSCC in order to evaluate the locoregional recurrence in OSCC cases. In high-risk OSCC cases, there may be an increased depth of invasion in resected tissues. We hypothesized that the correlation between Brandwein-Gensler criteria and depth of invasion can be used as an independent predictor for locoregional recurrence in OSCC.

Oral cancer diagnosis based on gated recurrent unit networks optimized by an improved version of Northern Goshawk optimization algorithm

Oral cancer early diagnosis is a critical task in the field of medical science, and one of the most necessary things is to develop sound and effective strategies for early detection. The current research investigates a new strategy to diagnose an oral cancer based upon combination of effective learning and medical imaging. The current research investigates a new strategy to diagnose an oral cancer using Gated Recurrent Unit (GRU) networks optimized by an improved model of the NGO (Northern Goshawk Optimization) algorithm. The proposed approach has several advantages over existing methods, including its ability to analyze large and complex datasets, its high accuracy, as well as its capacity to detect oral cancer at the very beginning stage. The improved NGO algorithm is utilized to improve the GRU network that helps to improve the performance of the network and increase the accuracy of the diagnosis. The paper describes the proposed approach and evaluates its performance using a dataset of oral cancer patients. The findings of the study demonstrate the efficiency of the suggested approach in accurately diagnosing oral cancer.

THE LONG ARM OF COVID-19

Chickenpox is often described as a benign disease. However, patients with varicella and coinfection with COVID-19 may develop severe respiratory infection. The interest in the presented case arose from the unusual fast progress of the chickenpox which led to a lethal outcome in a patient with unsuspected COVID-19, proven by PCR after his death. The underlying disease – cancer of the tongue was in remission and wasn’t a cause for immunosuppression which could be a reason for the clinical course of the disease. Aim: To present an interesting case of a 44-year-old man with a previous diagnosis of oral cancer who died after he was diagnosed with varicella and tested positive postmortem for COVID-19. Patient and method: A 44-year-old patient was admitted to the hospital urgently and stayed for 3 and a half hours in the clinic of infectious diseases of UMHAT "St. George", Plovdiv, with a diagnosis of varicella. The patient has accompanying diseases like oral cancer (root of the tongue) and hypertension. Epidemiological data are for contact and fresh infection with varicella, progressing rapidly and beginning of symptoms of respiratory insufficiency and lethal outcome. Due to the fulminant course and the short period of time, the positive PCR result for COVID-19 was proved after the patient died. Conclusions: Unsuspected comorbidity of COVID-19 and varicella without any other reason for immunosuppression could induce unusual severe course of the chickenpox.

Enhancing oral squamous cell carcinoma detection: a novel approach using improved EfficientNet architecture

ProblemOral squamous cell carcinoma (OSCC) is the eighth most prevalent cancer globally, leading to the loss of structural integrity within the oral cavity layers and membranes. Despite its high prevalence, early diagnosis is crucial for effective treatment.AimThis study aimed to utilize recent advancements in deep learning for medical image classification to automate the early diagnosis of oral histopathology images, thereby facilitating prompt and accurate detection of oral cancer.MethodsA deep learning convolutional neural network (CNN) model categorizes benign and malignant oral biopsy histopathological images. By leveraging 17 pretrained DL-CNN models, a two-step statistical analysis identified the pretrained EfficientNetB0 model as the most superior. Further enhancement of EfficientNetB0 was achieved by incorporating a dual attention network (DAN) into the model architecture.ResultsThe improved EfficientNetB0 model demonstrated impressive performance metrics, including an accuracy of 91.1%, sensitivity of 92.2%, specificity of 91.0%, precision of 91.3%, false-positive rate (FPR) of 1.12%, F1 score of 92.3%, Matthews correlation coefficient (MCC) of 90.1%, kappa of 88.8%, and computational time of 66.41%. Notably, this model surpasses the performance of state-of-the-art approaches in the field.ConclusionIntegrating deep learning techniques, specifically the enhanced EfficientNetB0 model with DAN, shows promising results for the automated early diagnosis of oral cancer through oral histopathology image analysis. This advancement has significant potential for improving the efficacy of oral cancer treatment strategies.

Uracil as a biomarker for spatial pyrimidine metabolism in the development of gingivobuccal oral squamous cell carcinoma

No biomarker has yet been identified that allows accurate diagnosis and prognosis of oral cancers. In this study, we investigated the presence of key metabolites in oral cancer using proton nuclear magnetic resonance (NMR) spectroscopy to identify metabolic biomarkers of gingivobuccal oral squamous cell carcinoma (GB-OSCC). NMR spectroscopy revealed that uracil was expressed in 83.09% of tumor tissues and pyrimidine metabolism was active in GB-OSCC; these results correlated well with immunohistochemistry (IHC) and RNA sequencing data. Based on further gene and protein analyses, we proposed a pathway for the production of uracil in GB-OSCC tissues. Uridinetriphosphate (UTP) is hydrolyzed to uridine diphosphate (UDP) by CD39 in the tumor microenvironment (TME). We hypothesized that UDP enters the cell with the help of the UDP-specific P2Y6 receptor for further processing by ENTPD4/5 to produce uracil. As the ATP reserves diminish, the weakened immune cells in the TME utilize pyrimidine metabolism as fuel for antitumor activity, and the same mechanism is hijacked by the tumor cells to promote their survival. Correspondingly, the differential expression of ENTPD4 and ENTPD5 in immune and tumor cells, respectively, indicatedtheir involvement in disease progression. Furthermore, higher uracil levels were detected in patients with lymph node metastasis, indicating that metastatic potential is increased in the presence of uracil. The presence of uracil and/or expression patterns of intermediate molecules in purine and pyrimidine pathways, such asCD39, CD73, and P2Y6 receptors together with ENTPD4 and ENTPD5, hold promise as biomarker(s) for oral cancer diagnosis and prognosis.

Nanomaterial Biosensors in Salivary Diagnosis of Oral Cancer: A Scoping Review

Nanomaterial Biosensors in Salivary Diagnosis of Oral Cancer: A Scoping Review

Use of digital strategies in the diagnosis of oral squamous cell carcinoma: a scoping review.

Telediagnosis uses information and communication technologies to support diagnosis, shortening geographical distances. It helps make decisions about various oral lesions. The objective of this scoping review was to map the existing literature on digital strategies to assist in the diagnosis of oral squamous cell carcinoma. this review was structured based on the 5-stage methodology proposed by Arksey and O'Malley, the Joanna Briggs Institute Manual for Evidence Synthesis and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. The methods were registered on the Open Science Framework. The research question was: What digital strategies have been used to assist in the diagnosis of oral squamous cell carcinoma? The search was conducted on PubMed/MEDLINE, Scopus, Web of Science, Embase, and ScienceDirect. Inclusion criteria comprised studies on telediagnosis, teleconsultation or teleconsultation mediated by a professional and studies in English, without date restrictions. The search conducted in June 2023 yielded 1,798 articles, from which 16 studies were included. Telediagnosis was reported in nine studies, involving data screening through applications, clinical images from digital cameras, mobile phones or artificial intelligence. Histopathological images were reported in four studies. Both, telediagnosis and teleconsultation, were mentioned in seven studies, utilizing images and information submission services to platforms, WhatsApp or applications. One study presented teleconsultations involving slides and another study introduced teleconsultation mediated by a professional. Digital strategies telediagnosis and teleconsultations enable the histopathological diagnosis of oral cancer through clinical or histopathological images. The higher the observed diagnostic agreement, the better the performance of the strategy.

Psychosocial Impact of Oral Cancer Diagnosis and Treatment: Role of Dental Professionals in Support and Rehabilitation- A Narrative Review

Psychosocial Impact of Oral Cancer Diagnosis and Treatment: Role of Dental Professionals in Support and Rehabilitation- A Narrative Review

Early Detection and Diagnosis of Oral Cancer Using Deep Neural Network

Oral squamous cell carcinoma (OSCC) is a malignancy that destroys the ability of the tissues surrounding the mouth to develop layers and membranes. Automated early diagnosis of oral histopathological images has allowed for the successful diagnosis of oral cancer, thanks to recent advancements in Deep Learning (DL) for biomedical image classification. By using a convolutional neural network (CNN) model based on deep learning for the initial analysis of oral squamous cell carcinoma (OSCC), this work aims to automate the classification of benign and malignant oral biopsy histopathological images. For this study, the CNN model Inception-Resnet-V2 is selected using the transfer learning approach. To enhance OSCC detection, additional layers are incorporated into this pretrained model. By mining a repository of oral cancer histopathology images, we can gauge how well these tweaked models perform. We examine the modified structure of the pre-trained Inception-Resnet-V2 model and suggest a DL-CNN model that uses it. With an accuracy of 91.78%, it has outperformed in terms of performance metrics.

Early Detection and Diagnosis of Oral Cancer Using Deep Neural Network

Oral squamous cell carcinoma (OSCC) is a malignancy that destroys the ability of the tissues surrounding the mouth to develop layers and membranes. Automated early diagnosis of oral histopathological images has allowed for the successful diagnosis of oral cancer, thanks to recent advancements in Deep Learning (DL) for biomedical image classification. By using a convolutional neural network (CNN) model based on deep learning for the initial analysis of oral squamous cell carcinoma (OSCC), this work aims to automate the classification of benign and malignant oral biopsy histopathological images. For this study, the CNN model Inception-Resnet-V2 is selected using the transfer learning approach. To enhance OSCC detection, additional layers are incorporated into this pretrained model. By mining a repository of oral cancer histopathology images, we can gauge how well these tweaked models perform. We examine the modified structure of the pre-trained Inception-Resnet-V2 model and suggest a DL-CNN model that uses it. With an accuracy of 91.78%, it has outperformed in terms of performance metrics.