Gastrointestinal image classification based on VGG16 and transfer learning

Data Analytic interpretation of plant species is not widely explored. The image variety of plant species can be investigated with the help of machine learning and deep learning techniques. Machine learning and deep learning make the plant species investigation automated and almost accurate. In this paper, we have proposed a novel native and invasive plant species classification technique. We use the pre-trained deep convolution neural networks (transfer learning) to extract features from plant species and classify them into Native and Invasive. Here, state-of-the-art deep convolution neural network models like InceptionV3, MobileNetV2, ResNetV2, VGG16, and Xception have been explored to extract the feature vectors from images. Furthermore, the features of the images have been used for image classification using the Random Forest algorithm. The performance of the proposed system is verified on the proposed dataset (Native-Invasive) with aerial view images of native and invasive species. Results were obtained using cross-validation techniques. Our experiments demonstrate the potential of our proposed method for achieving excellent performance with a 95% accuracy using Xception with data augmentation, hyper-parameter optimization, and the Random Forest classification technique. The aerial images were captured and labeled by us which is very novel to our experimental setup. After using deep learning models we got promising results. The dataset used to train the plant species classifier will be made available on request.KeywordsFeature extractionTransfer learningRandom ForestDeep learningImage classificationNative plant speciesInvasive plant species

Because of the success of deep learning in multiple sectors, it is gaining unquestionable acceptance in the therapeutic field also. The key challenge is determining how to construct a deep CNN model with an inadequate amount of training data. One key question is whether transfer learning and fine-tuning can be employed in biomedical image analysis to lessen the load of manual data labelling while still producing a complete deep representation for the task. In this paper, we compare the performance of transfer learning and machine learning for nuclei categorization to answer this question statistically. The used machine learning approaches are Decision Tree, Support Vector Machine, Quadratic Discriminant Analysis, K Neighbors, Ada-Boost, Gaussian Naïve Bayes, Logistic Regression, Extra Trees, Random Forest, Histogram Gradient Boosting. This paper shows how to recognize nuclei from microscope pictures using a deep learning model and an image processing-based processing flow. Convolutional neural networks and Inception Resnet V2 deep networks have been used to generate better results. The data augmentation is also done to address the paucity of data.

Purpose: To determine whether domain transfer learning can improve the performance of deep features extracted from digital mammograms using a pre-trained deep convolutional neural network (CNN) in the prediction of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. Method: In this study, we collected digital mammography magnification views for 140 patients with DCIS at biopsy, 35 of which were subsequently upstaged to invasive cancer. We utilized a deep CNN model that was pre-trained on two natural image data sets (ImageNet and DTD) and one mammographic data set (INbreast) as the feature extractor, hypothesizing that these data sets are increasingly more similar to our target task and will lead to better representations of deep features to describe DCIS lesions. Through a statistical pooling strategy, three sets of deep features were extracted using the CNNs at different levels of convolutional layers from the lesion areas. A logistic regression classifier was then trained to predict which tumors contain occult invasive disease. The generalization performance was assessed and compared using repeated random sub-sampling validation and receiver operating characteristic (ROC) curve analysis. Result: The best performance of deep features was from CNN model pre-trained on INbreast, and the proposed classifier using this set of deep features was able to achieve a median classification performance of ROC-AUC equal to 0.75, which is significantly better (p<=0.05) than the performance of deep features extracted using ImageNet data set (ROCAUC = 0.68). Conclusion: Transfer learning is helpful for learning a better representation of deep features, and improves the prediction of occult invasive disease in DCIS.

Machine learning models can analyze medical images in a more objective and complete manner since they are algorithmic and automated. Many research efforts have explored machine learning’s potential in the detection, classification, segmentation, and other difficulties associated with different illness states. To deal with the deficiency of available data for deep learning model training, a popular solution has been to use transfer learning (TL). In particular, the ImageNet pre-trained models have been one of the most prominent applications of TL. In this study, we use transfer learning to categorize several Epithelial tissues (e.g., nervous tissues, blood, etc.), each of which is made up of cells that have a common ancestry, a comparable cellular structure, and a shared function. As our pre-trained model in Transfer Learning, we have mostly concentrated on the Xception network, a widely used CNN architecture. Along with the suggested model, some more Transfer Learning architectures like VGG16, InceptionV3, and ResNet50 were also tried. The proposed Xception model achieved an accuracy of 88%, making it the most reliable option.

A multi-model data-fusion based deep transfer learning for improved remaining useful life estimation for IIOT based systems

Machine learning has been densely used in most computer-aided medical diagnosis systems. These systems not only supported the physician’s decision but also accelerate the necessitated procedures. Electroencephalography (EEG) is an essential device for measuring the brain’s electrical activities. EEG is used to detect a series of brain disorders such as epilepsy, dementia, Parkinson’s disease, and Schizophrenia (SZ). In this work, a novel method for detecting SZ using EEG recordings is suggested. Initially, the presented technique breaks down each channel of the input EEG recordings into EEG rhythms. The wavelet transform is employed to achieve this. The 1D local binary pattern (LBP) is then used to code the acquired rhythm signals. Each row of the input picture is formed by concatenating the uniform histograms of the 1D LBP coded beats. The rows of the images are formed from the channels of the input EEG signal, while the columns of the images are constructed from the rhythms. Extreme learning machines (ELM) based autoencoders (AE) are utilized at a data augmentation step. After data augmentation, the SZ and healthy cases are classified using well-known deep transfer learning. Deep transfer learning employs a variety of pre-trained deep Convolutional Neural Network (CNN) models. Various performance assessment indicators are used to evaluate the produced outcomes. An EEG dataset that Lomonosov Moscow State University released is used in experiments, and a 97.7% accuracy score is obtained. The obtained results are also compared with several recently published methods. The comparisons show that the proposed method outperforms the compared methods.

The evolution and improvements of deep learning are being used to tackle any research obstacles that could be converted into classification problems in all spheres of life. Each Deep convolutional neural network (DCNN) design's output is determined by the depth and value of the hyperparameters, which explains why so many of them have been proposed. These DCNN architectures must be created entirely from scratch, and they can only be used for the applications for which they were intended. Transfer learning may be used to modify these pre-trained networks so they are more appropriate for particular purposes. This article aims to evaluate the empirical performance of the applicability of pre-trained DCNN models to identify human face presentation threats (FPAD). Human FPAD is one of the most significant and crucial areas of research right now because of the introduction of ambient computing, which necessitates contact-free identification of persons with the help of their biometric traits. Six pre-trained DCNN models are taken into account for an experimental evaluation in human FPAD alias VGG19, VGG16, DensNet121, MobileNet, Xception, and InceptionV3. The investigation makes use of the NUAA and Replay-Attack benchmark FPAD datasets. Thepade's sorted block truncation coding (SBTC) 10-ary features are merged with deep learning features produced from the finest performing finetuned DCNNs to enhance the FPAD capabilities of analyzed machine learning (ML) classifiers. The integration of features of Thepade's SBTC 10-ary and DCNN has considerably increased the FPAD accuracy of ML classifiers with slightly more computations of feature extraction.

Early and accurate identification of brain tumors is essential for determining effective treatment strategies and improving patient outcomes. Artificial intelligence (AI) and deep learning (DL) techniques have shown promise in automating diagnostic tasks based on magnetic resonance imaging (MRI). This study evaluates the performance of four pre-trained deep convolutional neural network (CNN) architectures for the automatic multi-class classification of brain tumors into four categories: Glioma, Meningioma, Pituitary, and No Tumor. The proposed approach utilizes the publicly accessible Brain Tumor MRI Msoud dataset, consisting of 7023 images, with 5712 provided for training and 1311 for testing. To assess the impact of data availability, subsets containing 25%, 50%, 75%, and 100% of the training data were used. A stratified five-fold cross-validation technique was applied. The CNN architectures evaluated include DeiT3_base_patch16_224, Xception41, Inception_v4, and Swin_Tiny_Patch4_Window7_224, all fine-tuned using transfer learning. The training pipeline incorporated advanced preprocessing and image data augmentation techniques to enhance robustness and mitigate overfitting. Among the models tested, Swin_Tiny_Patch4_Window7_224 achieved the highest classification Accuracy of 99.24% on the test set using 75% of the training data. This model demonstrated superior generalization across all tumor classes and effectively addressed class imbalance issues. Furthermore, we deployed and benchmarked the best-performing DL model on embedded AI platforms (Jetson AGX Xavier and Orin Nano), demonstrating their capability for real-time inference and highlighting their feasibility for edge-based clinical deployment. The results highlight the strong potential of pre-trained deep CNN and transformer-based architectures in medical image analysis. The proposed approach provides a scalable and energy-efficient solution for automated brain tumor diagnosis, facilitating the integration of AI into clinical workflows.

Traditional Image Processing Techniques (IPT), used for automating the detection and classification of weld defects from radiography images, have their own limitations, which can be overcome by Deep Neural Networks (DNN). DNN produces considerably good results in fields which offer big dataset for it to train. DNN trained with small datasets by conventional methods produces less accurate results. This limits the use of DNN in many fields. This study focuses to overcome this limitation, by adopting transfer learning using Pre-trained deep convolutional neural networks. By this method, a weld defect radiographic image classifier, which can classify 14 types of weld defects, was constructed. 940 Image patches of weld defects were manually collected and labelled from GDXray database. Subsequently the features of this weld defect dataset were extracted using VGG16 and ResNet50 CNNs, both pre-trained on the ImageNet database. Then machine learning models such as Logistic Regression, Support Vector Machine (SVM) and Random Forest were trained on these extracted features. The Classifier based on SVM trained on features extracted by ResNet50 outperforms the other counter parts with an accuracy of 98%. In all these cases, transfer learning improves performance and reduces the training time and computational system requirements.

Major Depressive Disorder (MDD) is a widespread global mental disease. The Effectiveness of Selective Serotonin Reuptake Inhibitors (SSRIs) antidepressants prescribed for MDD patients is limited and pre-treatment assessment of treatment outcome is a vital task. In this study, a hybrid model based on Transfer Learning (TL) of powerful pre-trained deep Convolutional Neural Networks (CNNs) empowered with bidirectional Long Short-term Memory (BLSTM) cells and attention mechanism were developed to classify Responders (R) and Non-Responders (NR) to SSRI antidepressants, using raw data images of pre-treatment EEG signal obtained from 30 MDD patients. TL-LSTM-Attention models based on VGG16, Xception, and Densenet121 models were created for the classification of R/NR. Results show that the highest performance of hybrid TL-LSTM-Attention models was achieved by VGG16-LSTM-Attention with an accuracy of 98.21%, sensitivity of 96.22%, and specificity of 99.67%. An Ensemble model based on weighted majority voting among hybrid models is constructed to surpass the performance of each single hybrid model. Our proposed ensemble model gained accuracy, sensitivity, and specificity of 98.84%, 97.80%, and 99.60%, respectively. Hence, the proposed model which is an ensemble of TL-LSTM-Attention models fed by raw data images of EEG signal leads to high confidence in the prediction of antidepressants treatment outcome.

Classification of Brain Tumour Disease with Transfer Learning Using Modified Pre-trained Deep Convolutional Neural Network

Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison

Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.

Female accounts for approximately 50% of the total population worldwide and many of them had breast cancer. Computer-aided diagnosis frameworks could reduce the number of needless biopsies and the workload of radiologists. This research aims to detect benign and malignant tumors automatically using breast ultrasound (BUS) images. Accordingly, two pretrained deep convolutional neural network (CNN) models were employed for transfer learning using BUS images like AlexNet and DenseNet201. A total of 697 BUS images containing benign and malignant tumors are preprocessed and performed classification tasks using the transfer learning-based CNN models. The classification accuracy of the benign and malignant tasks is completed and achieved 92.8% accuracy using the DensNet201 model. The results thus achieved compared in state of the art using benchmark data set and concluded proposed model outperforms in accuracy from first stage breast tumor diagnosis. Finally, the proposed model could help radiologists diagnose benign and malignant tumors swiftly by screening suspected patients.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that severely impacts memory and cognitive functions. Early diagnosis remains crucial for timely intervention and care. This research aims to explore the use of artificial intelligence, specifically deep learning, for the early prediction and classification of AD using structural magnetic resonance imaging (MRI) images. A dataset comprising approximately 44,000 brain MRI images with four diagnostic classes (mild, moderate, severe, and very severe dementia) was used to train and evaluate multiple convolutional neural network (CNN) architectures. Three deep learning models were developed and tested: a custom CNN built from scratch, a spatial-channel convolutional attention network (SCCAN), and a pre-trained Visual Geometry Group VGG16 model using transfer learning. The methodology included extensive preprocessing, data augmentation, normalization, and a train–validation–test split to ensure robust performance. Evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrices were used to assess classification efficacy. Among the models tested, the Visual Geometry Group VGG16 model achieved the highest classification accuracy, closely followed by the SCCAN, while the custom CNN demonstrated competitive performance with fewer layers. Grad-CAM visualizations were integrated to provide insight into model decision-making, enhancing interpretability. The results confirm the effectiveness of deep learning in classifying early AD stages with high accuracy and support its integration into clinical diagnostic tools. However, the study also identifies limitations, including dataset diversity, class imbalance, and generalizability across diverse populations. Future research should consider using larger, multi-center datasets (including PET and EEG modalities). This project demonstrates that deep learning can offer reliable, scalable, and interpretable solutions for the early detection of AD, potentially transforming the diagnostic pathway and enabling earlier therapeutic interventions.