Inception Resnet V2 Model Research Articles

Deep Learning Models for Anatomical Location Classification in Esophagogastroduodenoscopy Images and Videos: A Quantitative Evaluation with Clinical Data

Background/Objectives: During gastroscopy, accurately identifying the anatomical locations of the gastrointestinal tract is crucial for developing diagnostic aids, such as lesion localization and blind spot alerts. Methods: This study utilized a dataset of 31,403 still images from 1000 patients with normal findings to annotate the anatomical locations within the images and develop a classification model. The model was then applied to videos of 20 esophagogastroduodenoscopy procedures, where it was validated for real-time location prediction. To address instability of predictions caused by independent frame-by-frame assessment, we implemented a hard-voting-based post-processing algorithm that aggregates results from seven consecutive frames, improving the overall accuracy. Results: Among the tested models, InceptionV3 demonstrated superior performance for still images, achieving an F1 score of 79.79%, precision of 80.57%, and recall of 80.08%. For video data, the InceptionResNetV2 model performed best, achieving an F1 score of 61.37%, precision of 73.08%, and recall of 57.21%. These results indicate that the deep learning models not only achieved high accuracy in position recognition for still images but also performed well on video data. Additionally, the post-processing algorithm effectively stabilized the predictions, highlighting its potential for real-time endoscopic applications. Conclusions: This study demonstrates the feasibility of predicting the gastrointestinal tract locations during gastroscopy and suggests a promising path for the development of advanced diagnostic aids to assist clinicians. Furthermore, the location information generated by this model can be leveraged in future technologies, such as automated report generation and supporting follow-up examinations for patients.

Amur Tiger Individual Identification Based on the Improved InceptionResNetV2.

Accurate and intelligent identification of rare and endangered individuals of flagship wildlife species, such as Amur tiger (Panthera tigris altaica), is crucial for understanding population structure and distribution, thereby facilitating targeted conservation measures. However, many mathematical modeling methods, including deep learning models, often yield unsatisfactory results. This paper proposes an individual recognition method for Amur tigers based on an improved InceptionResNetV2 model. Initially, the YOLOv5 model is employed to automatically detect and segment facial, left stripe, and right stripe areas from images of 107 individual Amur tigers, achieving a high average classification accuracy of 97.3%. By introducing a dropout layer and a dual-attention mechanism, we enhance the InceptionResNetV2 model to better capture the stripe features of individual tigers at various granularities and reduce overfitting during training. Experimental results demonstrate that our model outperforms other classic models, offering optimal recognition accuracy and ideal loss changes. The average recognition accuracy for different body part features is 95.36%, with left stripes achieving a peak accuracy of 99.37%. These results highlight the model's excellent recognition capabilities. Our research provides a valuable and practical approach to the individual identification of rare and endangered animals, offering significant potential for improving conservation efforts.

Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning.

Cervical cancer (CC) ranks as the fourth most common form of cancer affecting women, manifesting in the cervix. CC is caused by the Human papillomavirus (HPV) infection and is eradicated by vaccinating women from an early age. However, limited medical facilities present a significant challenge in mid- or low-income countries. It can improve the survivability rate and be successfully treated if the CC is detected at earlier stages. Current technological improvements allow for cost-effective, more sensitive, and rapid screening and treatment measures for CC. DL techniques are widely adopted for the automated detection of CC. DL techniques and architectures are used to detect CC and provide higher detection performance. This study offers the design of Enhanced Cervical Precancerous Lesions Detection and Classification using the Archimedes Optimization Algorithm with Transfer Learning (CPLDC-AOATL) algorithm. The CPLDC-AOATL algorithm aims to diagnose cervical cancer using medical images. At the preliminary stage, the CPLDC-AOATL technique involves a bilateral filtering (BF) technique to eliminate the noise in the input images. Besides, the CPLDC-AOATL technique applies the Inception-ResNetv2 model for the feature extraction process, and the use of AOA chose the hyperparameters. The CPLDC-AOATL technique involves a bidirectional long short-term memory (BiLSTM) model for the cancer detection process. The experimental outcome of the CPLDC-AOATL technique emphasized the superior accuracy outcome of 99.53% over other existing approaches under a benchmark dataset.

Automated detection of COVID-19 and pneumonia diseases using data mining and transfer learning algorithms with focal loss from chest X-ray images

Pneumonia is a lung infection that produces severe inflammation, which can be caused by viruses, bacteria, or fungi. The global occurrence of pneumonia cases has been notably affected by the outbreak of Coronavirus Disease 2019 (COVID-19). Recent studies have demonstrated the capability of Deep Learning (DL) algorithms in detecting COVID-19 and pneumonia in medical images like X-rays. In this study, we employ four different Chest X-ray (CXR) datasets to evaluate four pre-trained Convolutional Neural Network (CNN) models: InceptionResNet V2, Inception V3, Xception, and MobileNet. These models were fine-tuned using either the Cross-Entropy (CE) loss function only for balanced datasets or separately using both the CE and Focal Loss (FL) functions for imbalanced datasets. For the first dataset, the InceptionResNet V2 model achieved the highest multi-classification accuracy of 88.63%. The superior model for the second and fourth datasets is Inception V3, with 94.35% and 97.67% multi-classification accuracy, respectively. For the third dataset, the best model is Xception, with a binary classification accuracy of 100.00%. Our results emphasize the significance of using the FL function in solving class imbalance problems in DL models. Additionally, it highlights the effectiveness of individual DL models in detecting different pneumonia infections using different CXR datasets.

Socio-economic Challenges in COVID Detection using Transfer Learning-Based Methods

Healthcare systems are at risk of collapsing unless significant structural and transformative measures are taken. Despite the global economy generating an additional 40 million jobs in the health sector by 2030, the World Health Organization projects a shortage of 9.9 million physicians, nurses, and midwives during the same period (WHO, 2016). The core of innovation in the healthcare industry lies in automation systems, particularly in the realm of image detection. As the ratio of healthcare workers to patients decreases, the integration of robotics and artificial intelligence plays a crucial role in bridging the gap. These technologies not only compensate for the declining workforce but also bring a level of accuracy and precision that eliminates the potential for human error in image detection processes. In this paper we focus on the COVID-19 pandemic that presents significant socio-economic challenges, impacting various aspects of daily life, including health, the economy, and social development. The need for chest X-ray (CXR) scans is rising due to pneumonia being a critical and common complication of COVID-19. Early detection and diagnosis are pivotal in curbing the spread of the virus, prompting the utilization of the reverse transcription polymerase chain reaction (RT-PCR) as the predominant screening technology. Nevertheless, the task's complexity, time-consuming nature, and reported insensitivity in this research emphasize the need for alternative approaches. CXR is a widely employed screening tool for lung-related diseases due to its straightforward and cost-effective application. In this paper, we have deployed different transfer learning methods to detect COVID-19 using chest X-ray images such as VGG19, ResNet-50, and InceptionResnetV2. The findings of our results indicate that the fine-tuned model utilizing the transfer learning and data augmentation techniques enhances the efficiency of COVID-19 detection. We performed a comparison of pre-trained networks and identified the InceptionResNetV2 model as having the highest classification performance with an accuracy of 97.33%.

Content-based image retrieval of Indian traditional textile motifs using deep feature fusion

In the fast-paced fashion world, unique designs are like early birds, grabbing attention as online shopping surges. Fabric texture plays an immense role in selecting the perfect design. Indian Traditional textile motifs are pivotal, showing rich cultural origins and attracting worldwide art fanatics. Yet, technology-driven abstract forms are posing a challenge for them. The decline of handmade artistic ability due to computerization is concerning. Crafting new designs associated with the latest trends is time- consuming and requires diligence. In this work an interactive CBIR (content-based image retrieval) system is presented. It utilizes deep features from InceptionV3 and InceptionResNetV2 models to match query designs with a database of traditional Indian textiles. Its performance is tested with Caltech-101, Corel-1K state-of-the-art datasets, and Indian Textiles datasets and the results are shown to be finer than the existing approaches. The similarity-based fine-grained saliency maps (SBFGSM) approach is employed to visualize the importance of features. Our approach combines deep feature fusion with PCA dimensionality reduction and speeds up search using a clustering approach. Relevance feedback is employed to refine the retrievals. This tool is expected to benefit designers by accelerating the design cycles by bridging the gap between human creativity and A.I. assistance.

Classification of Diabetic Retinopathy using Pre-Trained Deep Learning Models

Diabetic Retinopathy (DR) stands as the leading cause of blindness globally, particularly affecting individuals between the ages of 20 and 70. This paper presents a Computer- Aided Diagnosis (CAD) system designed for the automatic classification of retinal images into five distinct classes: Normal, Mild, Moderate, Severe, and Proliferative Diabetic Retinopathy (PDR). The proposed system leverages Convolutional Neural Networks (CNNs) employing pre-trained deep learning models. Through the application of fine-tuning techniques, our model is trained on fundus images of diabetic retinopathy with resolutions of 350x350x3 and 224x224x3. Experimental results obtained on the Kaggle platform, utilizing resources comprising 4 CPUs, 17 GB RAM, and 1 GB Disk, demonstrate the efficacy of our approach. The achieved Area Under the Curve (AUC) values for CNN, MobileNet, VGG-16, InceptionV3, and InceptionResNetV2 models are 0.50, 0.70, 0.53, 0.63, and 0.69, respectively.

Classification of human protein cell images using deep neural networks

Previously, single classification models were mainly studied to classify human protein cell images, i.e., to identify a certain protein based on a set of different cells. However, a classifier can identify only one protein, in fact, a single cell usually consists of multiple proteins, and the proteins are not completely independent of each other. In this paper, we build a human protein cell classification model by multi-label learning. The logical relationship and distribution characteristics among the labels are analyzed to determine the different proteins contained in a set of different cells (i.e., containing multiple elements in the output space). In this paper, using human protein image data, we conducted comparison experiments on pre-trained Xception and InceptionResnet V2 to optimize the two models in terms of data augmentation, channel settings, and model structure. The results show that the Optimized InceptionResnet V2 model achieves high performance in the classification task. The final accuracy of the Optimized InceptionResnet V2 model we obtained reached 96.1%, which is a 2.82% improvement relative to that before the optimized model.

Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques.

Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and deep learning approaches has the potential to speed up cancer diagnosis processes by allowing researchers to analyse large patient databases quickly and affordably. This study introduces the Inception-ResNetV2 model with strategically incorporated local binary patterns (LBP) features to improve diagnostic accuracy for lung and colon cancer identification. The model is trained on histopathological images, and the integration of deep learning and texture-based features has demonstrated its exceptional performance with 99.98% accuracy. Importantly, the study employs explainable artificial intelligence (AI) through SHapley Additive exPlanations (SHAP) to unravel the complex inner workings of deep learning models, providing transparency in decision-making processes. This study highlights the potential to revolutionize cancer diagnosis in an era of more accurate and reliable medical assessments.

Colorization of Black and White Images Using a Hybrid Deep Learning Framework

With the development of deep learning algorithms and their great success in the field of computer vision, the field of automatic image colorization has witnessed significant improvements in accuracy and realism. This study introduces a novel deep learning-based method for colorizing black and white photographs, utilizing the powerful feature extraction of the InceptionResNetV2 model and the generative capabilities of autoencoders. A custom data generator was developed for efficient preprocessing, data augmentation, and batch processing, enhancing memory usage and scalability. The system encodes grayscale images and extracts high-level features, which are then fused and decoded into two color channels, combined with the original luminance to recreate the image in the LAB color space. The method demonstrates strong performance with a PSNR of 22.8154 and a SSIM of 0.9097, showcasing its potential for applications like historical image restoration and media enhancement.

Enzyme dosage detection to degrade feathers in edible bird’s nests: A comparative convolutional neural networks study

Edible Bird’s Nest (EBN), a costly food product made from swiftlet’s saliva, has encountered a longstanding problem of plucking the swiftlet’s feather from the nests. The destructive and inefficient manual process of plucking the feathers can be substituted with a serine protease enzyme alternative. Accurate detection of enzyme dosage is crucial for ensuring efficient feather degradation with cost-effective enzyme usage. This research employed the transfer learning method using pretrained Convolutional Neural Networks (Pt-CNN) to detect enzyme dosage based on EBN’s images. This study aimed to compare the image classification mechanisms, architectures, and performance of three Pt-CNN: Resnet50, InceptionResnetV2, and EfficientNetV2S. InceptionResnetV2, using parallel convolutions and residual networks, significantly contributes to learning rich informative features. Consequently, the InceptionResnetV2 model achieved the highest accuracy of 96.18%, while Resnet50 and EfficientNetV2S attained only 30.44% and 17.82%, respectively. The differences in architecture complexity, parameter count, dataset characteristics, and image resolution also play a role in the performance disparities among the models. The study’s findings aid future researchers in streamlining model selection when facing limited datasets by understanding the reasons for the model’s performance and contributing to a non-destructive and quick solution for EBN’s cleaning process.

Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and prevention of eventual blindness. Traditional diagnostic methods, relying on human interpretation of medical images, face challenges in terms of accuracy and efficiency. In the present research, we introduce a novel method that offers superior precision in DR diagnosis, compared to traditional methods, by employing advanced deep learning techniques. Central to this approach is the concept of transfer learning. This entails the utilization of pre-existing, well-established models, specifically InceptionResNetv2 and Inceptionv3, to extract features and fine-tune selected layers to cater to the unique requirements of this specific diagnostic task. Concurrently, we also present a newly devised model, DiaCNN, which is tailored for the classification of eye diseases. To prove the efficacy of the proposed methodology, we leveraged the Ocular Disease Intelligent Recognition (ODIR) dataset, which comprises eight different eye disease categories. The results are promising. The InceptionResNetv2 model, incorporating transfer learning, registered an impressive 97.5% accuracy in both the training and testing phases. Its counterpart, the Inceptionv3 model, achieved an even more commendable 99.7% accuracy during training, and 97.5% during testing. Remarkably, the DiaCNN model showcased unparalleled precision, achieving 100% accuracy in training and 98.3% in testing. These figures represent a significant leap in classification accuracy when juxtaposed with existing state-of-the-art diagnostic methods. Such advancements hold immense promise for the future, emphasizing the potential of our proposed technique to revolutionize the accuracy of DR and other eye disease diagnoses. By facilitating earlier detection and more timely interventions, this approach stands poised to significantly reduce the incidence of blindness associated with DR, thus heralding a new era of improved patient outcomes. Therefore, this work, through its novel approach and stellar results, not only pushes the boundaries of DR diagnostic accuracy but also promises a transformative impact in early detection and intervention, aiming to substantially diminish DR-induced blindness and champion enhanced patient care.

The classification of the bladder cancer based on Vision Transformers (ViT)

Bladder cancer is a prevalent malignancy with diverse subtypes, including invasive and non-invasive tissue. Accurate classification of these subtypes is crucial for personalized treatment and prognosis. In this paper, we present a comprehensive study on the classification of bladder cancer into into three classes, two of them are the malignant set as non invasive type and invasive type and one set is the normal bladder mucosa to be used as stander measurement for computer deep learning. We utilized a dataset containing histopathological images of bladder tissue samples, split into a training set (70%), a validation set (15%), and a test set (15%). Four different deep-learning architectures were evaluated for their performance in classifying bladder cancer, EfficientNetB2, InceptionResNetV2, InceptionV3, and ResNet50V2. Additionally, we explored the potential of Vision Transformers with two different configurations, ViT_B32 and ViT_B16, for this classification task. Our experimental results revealed significant variations in the models’ accuracies for classifying bladder cancer. The highest accuracy was achieved using the InceptionResNetV2 model, with an impressive accuracy of 98.73%. Vision Transformers also showed promising results, with ViT_B32 achieving an accuracy of 99.49%, and ViT_B16 achieving an accuracy of 99.23%. EfficientNetB2 and ResNet50V2 also exhibited competitive performances, achieving accuracies of 95.43% and 93%, respectively. In conclusion, our study demonstrates that deep learning models, particularly Vision Transformers (ViT_B32 and ViT_B16), can effectively classify bladder cancer into its three classes with high accuracy. These findings have potential implications for aiding clinical decision-making and improving patient outcomes in the field of oncology.

Improving the Accuracy of Otitis Media with Effusion Diagnosis in Pediatric Patients Using Deep Learning.

Otitis media with effusion (OME), primarily seen in children aged 2 years and younger, is characterized by the presence of fluid in the middle ear, often resulting in hearing loss and aural fullness. While deep learning networks have been explored to aid OME diagnosis, prior work did not often specify if pediatric images were used for training, causing uncertainties about their clinical relevance, especially due to important distinctions between the tympanic membranes of small children and adults. We trained cross-validated ResNet50, DenseNet201, InceptionV3, and InceptionResNetV2 models on 1150 pediatric tympanic membrane images from otoendoscopes to classify OME. When assessed using a separate dataset of 100 pediatric tympanic membrane images, the models achieved mean accuracies of 92.9% (ResNet50), 97.2% (DenseNet201), 96.0% (InceptionV3), and 94.8% (InceptionResNetV2), compared to the seven otolaryngologists that achieved accuracies between 84.0% and 69.0%. The results showed that even the worst-performing model trained on fold 3 of InceptionResNetV2 with an accuracy of 88.0% exceeded the accuracy of the highest-performing otolaryngologist at 84.0%. Our findings suggest that these specifically trained deep learning models can potentially enhance the clinical diagnosis of OME using pediatric otoendoscopic tympanic membrane images.

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

The vast majority of people in India, agriculture is their main line of work, and it has a large economic impact.. Soil is important for supplying vital nutrients to crops for better yield. Determining soil nutrients is certainly essential for selecting appropriate crops and monitoring growth. Common methods used by agriculturalists are inadequate to satisfy increasing demands and have to obstruct cultivating soil. For a better crop yield, agriculturalists must have an awareness regarding the soil nutrients for a specific crop. There comes a need for using Deep learning methods in soil analysis that would help farmers in the domain. This study introduces an Optimal Deep Convolutional Neural Network Fusion Model for Soil nutrient Type Classification (ODCNNF-STC) technique. The presented ODCNNF-STC technique examines the input soil images to classify them into different nutrients present in the soil. In this approach, the noise present in the soil images are initially filtered using a bilateral filter (BF) followed by contrast enhancement. The preprocessed soil images are fed to the model formed by the fusion of DenseNet201 and InceptionResNetV2 models extracting the soil images that can successfully differentiate soil nutrients. Finally, classification of soil nutrients were performed by three classifiers namely extreme learning machine (ELM), RMSProp optimizer-based 1DCNN, and RMSProp optimizer-based Stacked Auto Encoder (SAE). The experimental validation of ODCNNF-STC method is examined on real-time dataset of soil images with a maximum accuracy of 99.39% over recent methods.

HER2GAN: Overcome the Scarcity of HER2 Breast Cancer Dataset Based on Transfer Learning and GAN Model

IntroductionImmunohistochemistry (IHC) is crucial for breast cancer diagnosis, classification, and individualized treatment. IHC is used to measure the levels of expression of hormone receptors (estrogen and progesterone receptors), human epidermal growth factor receptor 2 (HER2), and other biomarkers, which are used to make treatment decisions and predict how well a patient will do. The evaluation of the breast cancer score on IHC slides, taking into account structural and morphological features as well as a scarcity of relevant data, is one of the most important issues in the IHC debate. Several recent studies have utilized machine learning and deep learning techniques to resolve these issues. Materials and MethodsThis paper introduces a new approach for addressing the issue based on supervised deep learning. A GAN-based model is proposed for generating high-quality HER2 images and identifying and classifying HER2 levels. Using transfer learning methodologies, the original and generated images were evaluated. Results and ConclusionAll of the models have been trained and evaluated using publicly accessible and private data sets, respectively. The InceptionV3 and InceptionResNetV2 models achieved a high accuracy of 93% with the combined generated and original images used for training and testing, demonstrating the exceptional quality of the details in the synthesized images.

Mouth and oral disease classification using InceptionResNetV2 method

Digital tools have greatly improved the detection and diagnosis of oral and dental disorders like cancer and gum disease. Lip or oral cavity cancer is more likely to develop in those with potentially malignant oral disorders. A potentially malignant disorder (PMD) and debilitating condition of the oral mucosa, oral submucous fibrosis (OSMF), can have devastating effects on one’s quality of life. Incorporating deep learning into diagnosing conditions affecting the mouth and oral cavity is challenging. Mouth and Oral Diseases Classification using InceptionResNetV2 Method was established in the current study to identify diseases such as gangivostomatitis (Gum), canker sores (CaS), cold sores (CoS), oral lichen planus (OLP), oral thrush (OT), mouth cancer (MC), and oral cancer (OC). The new collection, termed "Mouth and Oral Diseases" (MOD), comprises seven distinct categories of data. Compared to state-of-the-art approaches, the proposed InceptionResNetV2 model’s 99.51% accuracy is significantly higher.

Optimization Strategy on Deep Learning Model to Improve Fruit Freshness Recognition

The high fruit production during the harvest season is a challenge in the process of sorting fresh fruit and rotten fruit in plantations. Automatic fruit freshness classification based on deep learning can speed up the sorting process. However, building a model with high accuracy requires the right strategy based on the dataset's characteristics. This research aims to apply optimization strategies to deep learning models to improve model performance. The optimization strategy is implemented by optimizing the model using fine-tuning strategy by selecting the best parameters based on learning rate, optimizers, transfer learning, and data augmentation. The transfer learning process is applied based on the dataset's characteristics by training some parameters with a size of 30% and 60%, which were tested in four scenarios. The fine-tuning strategy is applied to three Deep Learning models, i.e., MobileNetv2, ResNet50, and InceptionResNetV2, which have various parameter sizes. Based on test results, fine-tuning strategy produces the best performance up to 100% with a learning rate of 0.01, the SGD optimizers on the InceptionResNetV2 model are trained on 60% of the parameters.

Automated fracture detection in the ulna and radius using deep learning on upper extremity radiographs.

This study aimed to detect single or multiple fractures in the ulna or radius using deep learning techniques fed on upper-extremity radiographs. The data set used in the retrospective study consisted of different types of upper extremity radiographs obtained from an open-source dataset, with 4,480 images with fractures and 4,383 images without fractures. All fractures involved the ulna or radius. The proposed method comprises two distinct stages. The initial phase, referred to as preprocessing, involved the removal of radiographic backgrounds, followed by the elimination of nonbone tissue. In the second phase, images consisting only of bone tissue were processed using deep learning models, such as RegNetX006, EfficientNet B0, and InceptionResNetV2. Thus, whether one or more fractures of the ulna or the radius are present was determined. To measure the performance of the proposed method, raw images, images generated by background deletion, and bone tissue removal were classified separately using RegNetX006, EfficientNet B0, and InceptionResNetV2 models. Performance was assessed by accuracy, F1 score, Matthew's correlation coefficient, receiver operating characteristic area under the curve, sensitivity, specificity, and precision using 10-fold cross-validation, which is a widely accepted technique in statistical analysis. The best classification performance was obtained with the proposed preprocessing and RegNetX006 architecture. The values obtained for various metrics were as follows: accuracy (0.9921), F1 score (0.9918), Matthew's correlation coefficient (0.9842), area under the curve (0.9918), sensitivity (0.9974), specificity (0.9863), and precision (0.9923). The proposed preprocessing method is able to detect fractures of the ulna and radius by artificial intelligence.

RiceNet: A deep convolutional neural network approach for classification of rice varieties

The cultivation of desired grain varieties holds immense significance as about 67% of the world’s population is associated with the agriculture sector. Unknowingly sowing the wrong variety of seeds may lead to a colossal waste of effort and money. Furthermore, the growing issue of rice grain adulteration in high-quality rice poses a threat to the trust of rice importers and exporters. While traditional methods are expensive, laborious, and error-prone, Computer Vision provides a good alternative that constitutes a current, advanced technology for image processing and data evaluation that holds tremendous promise and potential. In this research study, five varieties of rice grains including Jehlum Sr-1, Mushkibudji, Sr-2, and Sr-4 were collected from local grain and were used for research analysis. A computer vision system “RiceNet” contingent upon Deep Convolutional Neural Network (DCNN) framework has been designed for ameliorating the accuracy of identifying five unique groups of rice grain varieties. Deep Learning (DL) based pre-trained architectures including InceptionV3 and InceptionResNetV2 models were also adopted for classifying five specific groups of rice species. To optimize model parameters and alleviate back-propagation error during training, the Adam optimizer with a learning rate (lr) of 0.00003 has been employed to fine-tune the pre-trained InceptionV3 and ResNetInceptionV2 models. The proposed RiceNet architecture and pre-trained models were also compared with traditional ML approaches of HOG-SVM, SIFT-SVM, HOG-Logistic Regression(HOG-LR), SIFT-Logistic Regression(SIFT-LR), HOG-KNN, and SIFT-KNN for rice grain classification. With these experimentations at hand, it was observed that our proposed model “RiceNet” outperformed other approaches in similar computer vision tasks. The prediction accuracy outcome for the test dataset by HOG-SVM, SIFT-SVM, HOG-LR, SIFT-LR, HOG-KNN, and SIFT-KNN models were 66.0%, 65.33%, 62.67%, 65.0%, 54.0%, and 52.0% respectively. RiceNet, InceptionV3 and ResNetInceptionV2 have the best prediction accuracy of 94%, 84% and 81.333%. The remarkably high success rate of DCNN models makes them highly valuable and can be extended to endorse an integrated grain identification system that can operate in real-world situations.