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Significant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, and the amount of cancer-related information is rapidly expanding. The authors have developed a helpful support system using three distinct deep-learning models, ResNet-50, EfficientNet-B3, and ResNet-101, along with transfer learning, to predict lung cancer, thereby contributing to health and reducing the mortality rate associated with this condition. This offer aims to address the issue effectively. Using a dataset of 1,000 DICOM lung cancer images from the LIDC-IDRI repository, each image is classified into four different categories. Although deep learning is still making progress in its ability to analyze and understand cancer data, this research marks a significant step forward in the fight against cancer, promoting better health outcomes and potentially lowering the mortality rate. The Fusion Model, like all other models, achieved 100% precision in classifying Squamous Cells. The Fusion Model and ResNet-50 achieved a precision of 90%, closely followed by EfficientNet-B3 and ResNet-101 with slightly lower precision. To prevent overfitting and improve data collection and planning, the authors implemented a data extension strategy. The relationship between acquiring knowledge and reaching specific scores was also connected to advancing and addressing the issue of imprecise accuracy, ultimately contributing to advancements in health and a reduction in the mortality rate associated with lung cancer.

This study introduces a hybrid deep learning model integrating EfficientNet-B3 and Vision Transformer with an Attention Fusion mechanism for automated colon cancer detection using the Kvasir endoscopic dataset. The model leverages EfficientNet-B3’s strength in capturing fine-grained local textures and Vision Transformer’s ability to model global contextual relationships. A multi-head attention-based fusion block harmonizes these features, achieving comprehensive representations and enhanced classification stability. Model optimization was guided by the Matthews Correlation Coefficient (MCC), alongside evaluations of accuracy, F1-score, and Brier Score. Experimental results demonstrate a 96.2% accuracy and an MCC of 0.961, surpassing standalone baselines and existing benchmark architectures. Cross-validation confirmed robust generalization, while Grad-CAM analyses improved interpretability by visualizing salient histopathological regions influencing predictions. Despite slight overfitting tendencies, the model maintained strong performance across all eight image classes. These findings highlight the model’s ability to address limitations of single-architecture approaches by combining local and global feature extraction, offering rapid, objective, and reliable diagnostic support. The proposed framework shows significant promise for integration into computer-aided colonoscopy systems, paving the way for enhanced clinical diagnostics and reduced pathologist workload through AI-driven precision medicine.

The maintenance of agricultural productivity is critically dependent on the efficient and accurate identification of plant diseases. As observed, the manual inspection to the illness is often inefficient and error-prone, particularly under conditions such as inconsistent lighting, leaf deformities, and subtle distinctions between disease symptoms. To address these challenges, we introduce an enhanced crop disease classification framework that incorporates EfficientNet-B3 with an ancillary convolutional layer and a spatial attention module (ACSA). EfficientNet-B3 offers a strong foundation for feature extraction due to its compound scaling and efficient computation, while the spatial attention module improves classification accuracy by directing the model to focus on critical regions of diseased leaves. Additionally, the integration of ancillary convolutional layer to this architecture enhances the ability of the model to detect subtle disease variations. To further improve the adaptability, the proposed method incorporates a preprocessing and data augmentation techniques. Together, these enhancements create a more effective process for identifying disease pattern in wide range of plant species. The model was evaluated using an extensive crop disease dataset and against state-of-the-art methods such as EffiNet-TS, PlantXViT, and MobileNet V2 to assess its effectiveness. The proposed approach achieved an accuracy of 99.89% and a recall rate of 99.87%, demonstrating its suitability for crop classification with minimal computational overhead. Ablation studies further validate the significant contributions of the spatial attention module and the ancillary convolutional layer to the overall performance of the proposed model.

A new multistage encryption algorithm is proposed by integrating the deep neural network with a new 4D multi-scroll chaotic map to enhance the efficiency and improve the security of image transmission in the open channel. This combined network expands the key space and maintains the secrecy of the key with the multistage encryption algorithm. Initially, the image adaptive key generation process is implemented by the EfficientNet-B3 network to extract the features from the source image, which are then converted into hash values using SHA 256. The hash values are partitioned into four sections, and each section is normalized to give one distinct initial value for the generation of a multi-scroll chaotic sequence. The pseudo-parallel process routes the split source sub-image blocks (128⨯128) of plain text to branch 1 or branch 2, decided by the seed value of the chaotic sequence, increasing the high robustness against the differential and statistical attacks. Each branch contains row and column-wise permutations, bidirectional selective shuffling, and chaotic intra/inter-pixel diffusion in varying orders. The key image diffusion and dynamic DNA diffusion to the intermediate cypher image exhibit a strong avalanche effect. The simulation evaluation on the natural data set images demonstrates the large key space of 2 to the power of 674, high key sensitivity, uniform histogram with entropy value attains the critical values of 7.9, high NPCR value of 99.9%, UACI values with 33.46%, almost zero-pixel correlation and strong robustness to the cropping and noise attacks.

Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.

Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health.

We present a new method for multi-source semi-supervised domain adaptation in remote sensing scene classification. The method consists of a pre-trained convolutional neural network (CNN) model, namely EfficientNet-B3, for the extraction of highly discriminative features, followed by a classification module that learns feature prototypes for each class. Then, the classification module computes a cosine distance between feature vectors of target data samples and the feature prototypes. Finally, the proposed method ends with a Softmax activation function that converts the distances into class probabilities. The feature prototypes are also divided by a temperature parameter to normalize and control the classification module. The whole model is trained on both the unlabeled and labeled target samples. It is trained to predict the correct classes utilizing the standard cross-entropy loss computed over the labeled source and target samples. At the same time, the model is trained to learn domain invariant features using another loss function based on entropy computed over the unlabeled target samples. Unlike the standard cross-entropy loss, the new entropy loss function is computed on the model’s predicted probabilities and does not need the true labels. This entropy loss, called minimax loss, needs to be maximized with respect to the classification module to learn features that are domain-invariant (hence removing the data shift), and at the same time, it should be minimized with respect to the CNN feature extractor to learn discriminative features that are clustered around the class prototypes (in other words reducing intra-class variance). To accomplish these maximization and minimization processes at the same time, we use an adversarial training approach, where we alternate between the two processes. The model combines the standard cross-entropy loss and the new minimax entropy loss and optimizes them jointly. The proposed method is tested on four RS scene datasets, namely UC Merced, AID, RESISC45, and PatternNet, using two-source and three-source domain adaptation scenarios. The experimental results demonstrate the strong capability of the proposed method to achieve impressive performance despite using only a few (six in our case) labeled target samples per class. Its performance is already better than several state-of-the-art methods, including RevGrad, ADDA, Siamese-GAN, and MSCN.

The immune system’s overproduction of white blood cells (WBCs) results in the most common blood cancer, leukemia. It accounts for about 25% of childhood cancers and is one of the primary causes of death worldwide. The most well-known type of leukemia found in the human bone marrow is acute lymphoblastic leukemia (ALL). It is a disease that affects the bone marrow and kills white blood cells. Better treatment and a higher likelihood of survival can be helped by early and precise cancer detection. As a result, doctors can use computer-aided diagnostic (CAD) models to detect early leukemia effectively. In this research, we proposed a classification model based on the EfficientNet-B3 convolutional neural network (CNN) model to distinguish ALL as an automated model that automatically changes the learning rate (LR). We set up a custom LR that compared the loss value and training accuracy at the beginning of each epoch. We evaluated the proposed model on the C-NMC_Leukemia dataset. The dataset was pre-processed with normalization and balancing. The proposed model was evaluated and compared with recent classifiers. The proposed model’s average precision, recall, specificity, accuracy, and Disc similarity coefficient (DSC) were 98.29%, 97.83%, 97.82%, 98.31%, and 98.05%, respectively. Moreover, the proposed model was used to examine microscopic images of the blood to identify the malaria parasite. Our proposed model’s average precision, recall, specificity, accuracy, and DSC were 97.69%, 97.68%, 97.67%, 97.68%, and 97.68%, respectively. Therefore, the evaluation of the proposed model showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing models.

Isolated dynamic sign language recognition (IDSLR) has the potential to change accessibility and inclusion by enabling speech and/or hearing-impaired people to engage more completely in a variety of spheres of life, including social interactions, work, and more. IDSLR is a challenging task due to considering a sequence of image frame analysis with multiple linguistic features for a single gesture in cluttered backgrounds and an illumination variation environment. We have proposed a Hybrid Efficient Convolution (HEC) model that ensembles EfficientNet-B3 and a few modified layers as an alternative to traditional machine learning techniques with improved performances in cluttered backgrounds with illumination variation environments. The architecture of the HCE integrates pre-trained layers of EfficientNet-B3 loaded with customized weights and a new custom dense layer featuring 256 units, followed by batch normalization, dropout, and the final output layer. To enhance the robustness of the system, we employed the augmentation technique during pre-processing. Then, the system executes channel-wise feature transformation through point-wise convolution that reduces the computational complexity and increases the accuracy. The updated dense layer with 256 units processes the output from the standard EfficientNet-B3, shaping the model into a hybrid form to achieve better performance. We have created our own gesture dataset, called “BdSL_OPA_23_GESTURES,” which consists of 6000 video clips of 100 isolated dynamic Bangla Sign Language words, with 60 videos for each word from 20 different people in the cluttered background with illumination variation environments to train and evaluate the performances of the proposed model. We have considered 80% of the total dataset for training purpose, while the remaining 20% is dedicated to testing and validation. In a small number of epochs, our proposed HEC model achieves a superior accuracy of 93.17% on our created “BdSL_OPA_23_GESTURES” dataset. All the information of the proposed model with the dataset has been shared along with the scientific community to provide access publicly at: https://github.com/Prothoma2001/Bangla-Continuous-Sign-Language-Recognition.git .

Background/Objectives: Oral cancer, particularly oral squamous cell carcinoma (OSCC), ranks as the sixth most prevalent cancer globally, with rates of occurrence on the rise. The diagnosis of OSCC primarily depends on histopathological images (HIs), but this method can be time-intensive, expensive, and reliant on specialized expertise. Manual diagnosis often leads to inaccuracies and inconsistencies, highlighting the urgent need for automated and dependable diagnostic solutions to enhance early detection and treatment success. Methods: This research introduces a deep learning (DL) approach utilizing EfficientNet-B3, complemented by learning rate tuning (LRT), to identify OSCC from histopathological images. The model is designed to automatically modify the learning rate based on the accuracy and loss during training, which improves its overall performance. Results: When evaluated using the oral tumor dataset from the multi-cancer dataset, the model demonstrated impressive results, achieving an accuracy of 99.84% and a specificity of 99.92%, along with other strong performance metrics. Conclusions: These findings indicate its potential to simplify the diagnostic process, lower costs, and enhance patient outcomes in clinical settings.