Inception V3 Research Articles

CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection.

Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which utilizes deep learning methods to improve the detection of cataract states. The aim is to create a framework that is more lightweight and adaptable for use in environments or devices with limited memory or storage capacity. This involves reducing the number of trainable parameters while still allowing for effective learning of representations from data. Additionally, the framework is designed to be suitable for real-time or near-real-time applications where rapid inference is essential. This study utilizes cataract and normal images from the Ocular Disease Intelligent Recognition (ODIR) database. The suggested model employs smaller kernels, fewer training parameters, and layers to efficiently decrease the number of trainable parameters, thereby lowering computational costs and average running time compared to other pre-trained models such as VGG19, ResNet50, DenseNet201, MIRNet, Inception V3, Xception, and Efficient net B0. The experimental results illustrate that the proposed approach achieves a binary classification accuracy of 97.24% (normal or cataract) and an average cataract state detection accuracy of 98.17% (normal, grade 1-minimal cloudiness, grade 2-immature cataract, grade 3-mature cataract, and grade 4-hyper mature cataract), competing with state-of-the-art cataract detection methods. The resulting model is lightweight at 17 MB and has fewer trainable parameters (175, 617), making it suitable for deployment in environments or devices with constrained memory or storage capacity. With a runtime of 212 ms, it is well-suited for real-time or near-real-time applications requiring rapid inference.

205 Detection of anemic sheep using ocular conjunctiva images and deep learning technology

Abstract In sheep, severe anemia often results from gastrointestinal nematodes infections, commonly caused by Haemonchus contortus, a blood-sucking nematode. The objective of the study was to develop a model to predict packed cell volume (PCV) in sheep through ocular conjunctiva images as a real-time anemia diagnosis approach. We collected 3,291 ocular conjunctiva images from 392 sheep on three different farms using an iPhone 8 (12-megapixel). To identify the region of interest in the images (ocular conjunctiva), we annotated 480 images using the Segment Anything Model (SAM). Subsequently, we employed an image segmentation algorithm based on U-net, utilizing the original images and annotations obtained from SAM. We then cropped the segmented images to retain only the region containing the ocular conjunctiva. These cropped and segmented images were used as input data, with PCV as the target variable, in both classification and regression models, while exploring three different deep neural network (DNN) architectures: VGG19, Inception v3, and Xception. We set a threshold of 27% (anemic < 27%, non-anemic ≥ 27%) to convert PCV into a binary variable for classification models. The dataset was split in train, validation and test sets using random by sheep strategy to perform the analysis of the segmentation and prediction models. The segmentation was tested using intersection over union (IoU) as an evaluation metric. To assess and compare predictive quality in the test set, we calculated accuracy, precision, recall, and F1 score for classification models, and R2 for regression models. The U-net segmentation model demonstrated good localization accuracy and reliable segmentation ability, with an average IoU of 0.93, 0.84 and 0.68 in the train, validation and test set, respectively. VGG19 outperformed the other models in classifying individuals as anemic or non-anemic, achieving an F1 score of 0.60, indicating its moderate ability to distinguish between these two classes. For regression, Xception provided the best performance with an R2 of 0.29, suggesting that 29% of the variance in PCV can be explained by the model’s predictions. This innovative approach not only expands the possibilities for integrated high-throughput phenotyping through computer vision but also aids in identifying anemic animals. The results suggest that using ocular conjunctiva images and DNN technology can contribute to support farm-level management decisions, and potentially reduce economic losses due to parasitic infections like H. contortus. Furthermore, this approach reduces the cost of blood tests for anemia identification while allowing for real-time anemia diagnosis.

Implementing Inception v3, VGG-16 and VGG-19 Architectures of CNN for Medicinal Plant leaves Identification and Disease Detection

Medicinal plants have been a valuable source of healing and healthcare for centuries, serving as a crucial component of traditional medicine systems worldwide. The identification of these plants is a fundamental step in harnessing their therapeutic potential and ensuring their sustainable use. Accurate identification of these plants and the early detection of diseases affecting their leaves are crucial for ensuring a consistent supply of high-quality medicinal resources. In this paper, three types of CNN architectures of deep learning are used to develop robust classification models for distinguishing between healthy and diseased medicinal plant leaves. Inception v3 is known for its versatility in handling various input image sizes without the need for extensive pre-processing, while VGG-19 exhibits high accuracy, robust feature extraction, and suitability for complex image patterns. VGG-16, known for its simplicity and ease of training, provided competitive results, particularly where computational resources were limited. These networks have been pre-trained on large-scale image datasets and fine-tuned using them. These different approaches provide a varied solution to this problem by comparing their accuracy levels, feasibility and to select which one suits the best in this study.

Plant diseases detection and classifications using SympDense- A fine-tuning deep learning model

A nation’s progress is directly linked to the effective functioning of its agricultural sector. The detection and classification of plant disease is an essential component of the agricultural industry. Plant diseases may result in substantial financial losses due to decreased crop production. As per the Food and Agriculture Organization of the United Nations, it is estimated that plant diseases result in a reduction of approximately 10-16% in global crop yields annually. Farmers are traditionally relying on visual inspection, using naked eye observation, as the primary method for detecting plant diseases. This involves a meticulous examination of crops to identify any visible signs of diseases. However, manual disease detection can lead to delayed identification, resulting in significant crop losses. Various methods, coupled with machine learning classifiers, were demonstrated effectiveness in scenarios involving manual feature extraction and limited datasets. However, to handle larger datasets, deep learning models such as Inception V4, ResNet-152, EfficientNet-B5, and DenseNet-201 were studied and implemented. Among these models, DenseNet-201 exhibited superior performance and accuracy compared to the previous methodology. Additionally, A Fine-tuning Deep Learning Model called SympDense was developed, which surpassed other deep learning models in terms of accuracy.

Convolutional Neural Network in Motion Detection for Physiotherapy Exercise Movement

Physiotherapy focuses on movement and optimal utilization of the patient's potential. Exercise Therapy is a physiotherapy procedure that specifically focuses exercises on active and passive movements. Cerebral Palsy (CP) patients are one of the sufferers of motor disorders of the upper extremities. Cerebral Palsy (CP) patients suffer from disorders in motor functions of the upper extremities. Physiotherapy Exercise Movement has 4 categories of movement exercises for the therapy of people with upper extremity body disorders: Elbow flexor strengthening in sitting using free weights, lifting an object up, reaching diagonally in sitting, and reaching from a low surface to a high surface. By taking 4 categories of motion movements in exercise therapy, data were taken using normal child subjects as standard movements, which then became a reference for CP child therapy. The limitations of therapy in physical care prompted researchers to investigate the use of image processing as input to Human Computer Interaction (HCI) in the process of motion detection-based therapy. In research using Deep learning as a classifier, namely using the CNN Model (Inception V3, Resnet152, and VGG16 architectural models). The results obtained by the CNN (Inception V3) model have the best performance with an accuracy percentage of 98%.

Multi-modal fusion deep transfer learning for accurate brain tumor classification using magnetic resonance imaging images

Early identification and treatment of brain tumors depend critically on accurate classification. Accurate brain tumor classification in medical imaging is essential for clinical decisions and individualized treatment plans. This paper introduces a novel method for classifying brain tumors called multimodal fusion deep transfer learning (MMFDTL) using original, contoured, and annotated magnetic resonance imaging (MRI) images to showcase its capabilities. The MMFDTL can capture complex tumor features frequently missed in analyzing individual modalities. The MMFDTL model employs three deep learning models for extracting features VGG16, Inception V3, and ResNet 50. The accuracy rate improves when combined with decision based multimodal fusion. It produces impressive outcomes of sensitivity 92.96%, specificity 98.54%, precision 93.60%, accuracy 98.80%, F1-score 93.26%, and kappa 91.86%. This research can improve medical imaging and brain tumor analysis through its multi modal fusion approach. It could give healthcare practitioners vital insights for personalized treatment plans and informed decision making.

Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas.

Prompt histopathological diagnosis with accuracy is required for soft tissue sarcomas (STSs) which are still challenging. In addition, the advances in artificial intelligence (AI) along with the development of pathology slides digitization may empower the demand for the prediction of behavior of STSs. In this article, we explored the application of deep learning for prediction of prognosis from histopathological images in patients with STS. Our retrospective study included a total of 35 histopathological slides from patients with STS. We trained Inception v3 which is proposed method of convolutional neural network based survivability estimation. F1 score which identify the accuracy and area under the receiver operating characteristic curve (AUC) served as main outcome measures from a 4-fold validation. The cohort included 35 patients with a mean age of 64 years, and the mean follow-up period was 34 months (2-66 months). Our deep learning method achieved AUC of 0.974 and an accuracy of 91.9% in predicting overall survival. Concerning with the prediction of metastasis-free survival, the accuracy was 84.2% with the AUC of 0.852. AI might be used to help pathologists with accurate prognosis prediction. This study could substantially improve the clinical management of patients with STS.

Deep Learning-Based Lung Medical Image Recognition

Pulmonary nodules serve as critical indicators for early lung cancer diagnosis, making their detection and classification essential. The prevalent use of transfer learning in recognition algorithms often encounters a significant disparity between source and target datasets, which hampers effective feature extraction from pulmonary nodules and degrades performance. An enhanced neural network model leveraging convolutional neural networks is introduced to address this issue. This model integrates a pre-trained GoogLeNet Inception V3 network with a custom-designed feature fusion layer, improving the network’s ability to extract features. To ascertain the optimal configuration, the models were evaluated based on accuracy in various combinations. The experiments conducted on the LUNA16 pulmonary nodule dataset revealed that the refined network model achieved an accuracy of 88.78% and a sensitivity of 87.18%. This represents an increase of 2.7 and 2.22 percentage points in accuracy and sensitivity, respectively, compared to the GoogLeNet Inception V3 algorithm. Further tests across different dataset proportions also yielded superior outcomes, demonstrating enhanced generalization capabilities. These findings can offer objective benchmarks for clinical diagnosis.

Classification of Brain Tumor Diseases Using Data Augmentation and Transfer Learning

An accumulation of aberrant cells called a brain tumor is the outcome of unregulated cell division. Brain cancers can be found using magnetic resonance imaging (MRI). The exponential growth of deep learning networks has allowed us to tackle complex tasks, even in fields as complicated as medicine. However, using these models requires a large corpus of data for the networks to be highly generalizable and have high performance. This dearth of training data makes it critical to explore methods such as data augmentation. In this sense, data augmentation methods are widely used in strategies to train networks, and with small data sets being vital in medicine due to the limited access to data, this work aims to identify the best classification system by considering the prediction accuracy in this vein. Data augmentation is performed on the database and fed into the three convolutional neural network (CNN) models. A comparison line is drawn between the three models based on accuracy and performance on the Inception v3 models, Mobile Net V2, and Squeeze Net network for brain tumor detection and classifying 350 brain MR images. The statistical methods were modified in order to evaluate these algorithms. With 0.992% accuracy, 0.993% recall, 0.989% precision, and 0.994% F1 score, the Squeeze Net model performed the best. The Mobile Net V2 model, which had an accuracy of 0.964%, came next. When the research's findings were compared to those of related studies in the literature, they revealed better success rates than those of the majority of investigations.

Leveraging Inception V3 for Precise Early and Late Blight Disease Classification in Potato Crops

Leveraging Inception V3 for Precise Early and Late Blight Disease Classification in Potato Crops

Diabetic Retinopathy Stage Detection Using CNN and Inception V3

The project explores the deployment of Convolutional Neural Networks (CNN) and the Inception V3 model for the automated detection and classification of diabetic retinopathy stages using fundus images. Recognizing diabetic retinopathy as a leading cause of blindness among the working-age population globally, this research aims to streamline the diagnostic process, traditionally reliant on the manual examination by ophthalmologists. Through the utilization of the DRIVE and STARE datasets, the project benchmarks the performance of CNN and Inception V3 models in accurately categorizing the severity of diabetic retinopathy into five distinct stages. The comparison between these models is grounded on parameters such as accuracy, loss, and predicted value, with findings indicating Inception V3's superiority in both performance metrics and diagnostic precision. This advancement could significantly contribute to early and more accessible detection of diabetic retinopathy, thereby mitigating progression towards blindness. Furthermore, the project underscores the potential of deep learning algorithms in enhancing diagnostic methodologies for retinal diseases, paving the way for future explorations in the field of medical imaging and artificial intelligence.

LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity.

Maize leaf diseases significantly impact yield and quality. However, recognizing these diseases from images taken in natural environments is challenging due to complex backgrounds and high similarity of disease spots between classes.This study proposes a lightweight multi-level attention fusion network (LFMNet) which can identify maize leaf diseases with high similarity in natural environment. The main components of LFMNet are PMFFM and MAttion blocks, with three key improvements relative to existing essential blocks. First, it improves the adaptability to the change of maize leaf disease scale through the dense connection of partial convolution with different expansion rates and reduces the parameters at the same time. The second improvement is that it replaces a adaptable pooling kernel according to the size of the input feature map on the original PPA, and the convolution layer to reshape to enhance the feature extraction of maize leaves under complex background. The third improvement is that it replaces different pooling kernels to obtain features of different scales based on GMDC and generate feature weighting matrix to enhance important regional features. Experimental results show that the accuracy of the LFMNet model on the test dataset reaches 94.12%, which is better than the existing heavyweight networks, such as ResNet50 and Inception v3, and lightweight networks such as DenseNet 121,MobileNet(V3-large) and ShuffleNet V2. The number of parameters is only 0.88m, which is better than the current mainstream lightweight network. It is also effective to identify the disease types with similar disease spots in leaves.

Strawberry disease detection using transfer learning of deep convolutional neural networks

The impact of disease on strawberry quality and yield holds considerable significance, prompting researchers to explore effective methodologies for disease detection in strawberries. Among these, deep learning has emerged as a pivotal approach. In this regard, this research explored the utilization of transfer learning in deep convolutional neural networks (CNNs) to identify various strawberry diseases. Specifically, we utilized models pre-trained on the ImageNet dataset, namely VGG19, Inception V3, ResNet50, and DenseNet121 architectures, employing both fine-tuning and feature extraction techniques of transfer learning and consequently compared to the models without transfer learning. The target diseases for identification included angular leaf spot, anthracnose, gray mold, and powdery mildew on both fruit and leaves. The study outcomes revealed that Resnet-50 consistently achieved the highest accuracy across all three configurations, achieving its peak accuracy at 94.4 %, followed by Densenet-121 with an accuracy of 94.1 % attained through fine-tuning. These results highlighted the superior performance of fine-tuned models over using these models solely as feature extractors for identifying strawberry diseases. Furthermore, this study revealed that the application of transfer learning substantially reduced training time and resulted in a lower count of trainable parameters than models trained without transfer learning. These outcomes strongly endorse the practicality and effectiveness of employing transfer learning techniques for precise strawberry disease identification. Additionally, further research can explore the application of transfer learning to a broader range of crops and diseases, potentially enhancing agricultural disease detection methodologies.

LEVERAGING DEEP LEARNING FOR ACCURATE RICE LEAF DISEASE RECOGNITION

: Imagine a Rice Field, seemingly healthy, yet harboring invisible foes. This paper delves into the world of crop disease detection for the Tan spot, Leaf blight, Sheath decay, Bacterial leaf rot, and False smut Dataset consists of 3546 images. The image segmentation is done by BIRCH clustering followed by GMM clustering; various texture features have been extracted through this. The classification is done using existing models such as VGG16, VGG19, RESNET50, and INCEPTION V3. After comparing various models and validation, the observation was made that VGG19 performs well compared to other models. The overall accuracy obtained for the VGG19 is 95.88.%. The achieved accuracy surpasses that of conventional backpropagation neural network models, indicating significant advancements in crop disease diagnosis. This study introduces a novel approach that opens avenues for future research in the field of deep learning for crop disease diagnosis.

Inappropriate YouTube content detection and classification by using proposed novel auto-determined k-means clustering and PDBRNN architecture

To ensure a safe and pleasant user experience while watching content on YouTube, it is necessary to identify and classify inappropriate content, especially content that is inappropriate for children. In this work, we have concentrated on establishing an efficient system for detecting inappropriate content on YouTube. Most of the work focuses on manual pre-processing; however, it takes too much time, requires manpower support, and is not ideal for solving real-time problems. To address this challenge, we have proposed an automatic preprocessing scheme for selecting appropriate frames and removing unwanted frames such as noise and duplicate frames. For this purpose, we have utilized the proposed novel auto-determined k-means (PADK-means) algorithm. Our PADK-means algorithm automatically determines the optimal cluster count instead of manual specifications. By doing this, we have solved the manual cluster count specification problem in the traditional k-means clustering algorithm. On the other hand, to improve the system’s performance, we utilized the Proposed Feature Extraction (PFE) method, which includes two pre-trained models DenseNet121 and Inception V3 are utilized to extract local and global features from the frame. Finally, we employ a proposed double-branch recurrent network (PDBRNN) architecture, which includes bi-LSTM and GRU, to classify the video as appropriate or inappropriate. Our proposed automatic preprocessing mechanism, proposed feature extraction method, and proposed double-branch RNN classifier yielded an impressive accuracy of 97.9%.

A novel method for indian vehicle registration number plate detection and recognition using CNN

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as “extraction,” “pre-processing,” and “segmentation,” and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard.

Deep learning for enhanced brain Tumor Detection and classification

The purpose of this research is to build an automated, robust, intelligent and hybrid system for the early diagnosis and classifying of brain tumor. To serve this purpose, the authors propose the Auto Contrast Enhancer, Tumor Detector and Classifier to efficiently provide on-demand contrast improvement of poor contrast MRI images for the early diagnosis and classification of brain tumors. The classifier accomplishes its task through a two-phase approach. During the initial phase, ODTWCHE is employed to enhance image contrast, facilitating accurate diagnosis of brain tumors. In the subsequent phase, the classifier leverages the power of deep transfer learning, utilizing the pre-trained Inception V3 model to refine the diagnostic process further. tumor classification. Compared to state-of-the-art models, including AlexNet, VGG-16, DenseNet-201, VGG-19, GoogLeNet, and ResNet-50, the proposed system showcased its outstanding performance by achieving the highest accuracy of 98.89 % on a public dataset that consists of MRI images with varying contrast and brightness levels. The precise detection and classification achieved on this multicolored dataset prove the system's robustness. The authors of the article address the usage of metrics in a variety of contexts, including academia, as well as the possible problems that may result from their improper application. They emphasize how crucial it is to create measurements that align with the system's objectives and to reduce any negative consequences that can skew the data or allow people to manipulate the system's incentives. The authors provide a thorough process for creating metrics that takes into account design considerations, countermeasures for unfavorable effects, and crucial requirements. The paper provides answers for the creation of metrics and gives examples of metrics' failures in many fields. The authors emphasize the significance of understanding how the goal and the data at hand relate to one another, as well as the necessity of compromise and clarity when goals are contradictory or incoherent.A comparative analysis with existing models further confirms that the proposed system consistently outperforms the competition.

Classification of different types of DDR RAM using transfer learning in convolutional learning networks

Technology, specifically computers play an important role in modern society. People who are new to computers can determine what type of RAM they have, which can be used to avoid confusion on what type of RAM their computer needs with the help of an Android device. For this study, a powerful computer with a Graphics Processing Unit (GPU) needed to be used to shorten the amount of time that the deep learning process takes. The study gathered images of 4 types of Random Access Memory for a RAM classification system. There were 1000 images in total for DDR1, DDR2, DDR3, and DDR4 RAM. The study utilized transfer learning to RAM type classification with pre-trained models such as VGG16, VGG19, Inception V3, and Xception. The data that was gathered showed that Xception is the best classifier with an initial average accuracy of 85.034% and a 100% Val_Accuracy even though the model had the longest loading time with 12 seconds.

An efficient approach for automatic crack detection using deep learning

PurposeAutomation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.Design/methodology/approachIn this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.FindingsA comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.Originality/valueThe proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer

Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98.986%.