How artificial intelligence deep learning models can be used to accurately determine lung cancers

In the realm of computer vision, image classification is a critical issue with many applications, including multimedia content analysis, security and surveillance, and medical imaging. The accuracy of image classification algorithms has considerably increased with the development of deep learning. The discipline of image classification has dramatically benefited from the usage of pre-trained Convolutional Neural Network (CNN) models. In this study, we perform image classification on the Wang dataset composed of 1000 images separated into ten categories, using six different pre-trained CNN models. The research made use of pre-trained versions of VGG16, Densenet, Mobilenet, Inception V3, Resnet50, and Xception models. We assessed the model performances in terms of training and testing accuracy. Testing accuracies for ten unique batches of data were calculated and averaged out. Densenet outperformed state-of-the-art models like Xception by a small margin, mainly due to low data availability. The findings of this study show how pre-trained models have advanced the field of image classification and shed light on how well these models perform in diverse image classification tasks. This study can serve as a valuable reference for researchers and practitioners in the field of computer vision and deep learning and help inform their choices when selecting pre-trained models for image classification, depending on their needs, while also considering data availability and computational constraints.

Deep neural networks such as Convolutional Neural Networks (CNNs) have achieved several significant milestones in visual data analytics. Benefited from transfer learning, many researchers use pre-trained CNN models to accelerate the training process. However, there is still uncertainty about the deep learning models, structures, and applications. For instance, the diversity of the datasets may affect the performance of each pre-trained model. Therefore, in this paper, we proposed a new approach based on genetic algorithms to select or regenerate the best pre-trained CNN models for different visual datasets. A new genetic encoding model is presented which denotes different pre-trained models in our population. During the evolutionary process, the optimal genetic code that represents the best model is selected, or new competitive individuals are generated using the genetic operations. The experimental results illustrate the effectiveness of the proposed framework which outperforms several existing approaches in visual data classification.

Emotions expressed by humans can be identified from facial expressions, speech signals, or physiological signals. Among them, the use of physiological signals for emotion classification is a notable emerging area of research. In emotion recognition, a person’s electrocardiogram (ECG) and galvanic skin response (GSR) signals cannot be manipulated, unlike facial and voice signals. Moreover, wearables such as smartwatches and wristbands enable the detection of emotions in people’s naturalistic environment. During the COVID-19 pandemic, it was necessary to detect people’s emotions in order to ensure that appropriate actions were taken according to the prevailing situation and achieve societal balance. Experimentally, the duration of the emotion stimulus period and the social and non-social contexts of participants influence the emotion classification process. Hence, classification of emotions when participants are exposed to the elicitation process for a longer duration and taking into consideration the social context needs to be explored. This work explores the classification of emotions using five pretrained convolutional neural network (CNN) models: MobileNet, NASNetMobile, DenseNet 201, InceptionResnetV2, and EfficientNetB7. The continuous wavelet transform (CWT) coefficients were detected from ECG and GSR recordings from the AMIGOS database with suitable filtering. Scalograms of the sum of frequency coefficients versus time were obtained and converted into images. Emotions were classified using the pre-trained CNN models. The valence and arousal emotion classification accuracy obtained using ECG and GSR data were, respectively, 91.27% and 91.45% using the InceptionResnetV2 CNN classifier and 99.19% and 98.39% using the MobileNet CNN classifier. Other studies have not explored the use of scalograms to represent ECG and GSR CWT features for emotion classification using deep learning models. Additionally, this study provides a novel classification of emotions built on individual and group settings using ECG data. When the participants watched long-duration emotion elicitation videos individually and in groups, the accuracy was around 99.8%. MobileNet had the highest accuracy and shortest execution time. These subject-independent classification methods enable emotion classification independent of varying human behavior.

Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework

Lung cancer is one of the most prevalent and lethal malignant tumors globally, characterized by high morbidity and mortality rates. Early diagnosis is crucial for enhancing treatment efficacy, reducing mortality, and improving patients’ quality of life. Traditional manual interpretation of computed tomography (CT) images—the primary screening tool for lung cancer—is often limited by subjectivity, heavy workload, and potential missed diagnoses. Artificial Intelligence (AI) methodologies have emerged as indispensable assets for radiologists, empowering them to conduct in-depth analyses of lung CT scans. This technology facilitates a more streamlined and unbiased evaluation of anatomical structures and pathological features depicted in chest CT images. Driven by advances in deep learning models such as convolutional neural networks (CNNs), the accuracy and robustness of AI methods for lung cancer diagnosis have been significantly improved, providing reliable references for clinical practice. This paper presents a brief review of the applications of AI methods in lung cancer diagnosis based on CT imaging in recent years. It systematically organizes and elaborates on the applications of AI methods represented by deep learning in pulmonary nodule detection, lung tumor segmentation, benign-malignant classification of lung cancer, and lung cancer-related gene mutation detection. Additionally, it analyzes the performance of different models on relevant datasets. Furthermore, the limitations of AI in CT imaging-based lung cancer diagnosis and future research directions are summarized. Through this survey, we aim to provide a concise and comprehensive overview of the current status of lung cancer diagnosis using CT-AI integrated systems, and ultimately promote the development and clinical application of these technologies in the early and accurate detection of lung cancer.

Fingerprint classification helps in reducing the number of comparisons during the matching stage in automatic fingerprint identification system. In this study, a convolutional neural network model is proposed for classification of plain, rolled and latent fingerprints. We first propose a new convolutional neural network model initialised with random weights and train the model on fingerprint images. Then we fine-tune two pre-trained convolutional neural network models on fingerprint images. Finally, we compare these three models: two pre-trained models and a custom convolutional neural network model initialised with random weights. We show that pre-trained models can achieve the state-of-the-art results on other similar tasks with no or little fine-tuning. We also show that training data size and depth of the network have a serious impact on the overall performance of deep networks. Dropout is used to enhance the performance of deep networks where the labelled training data is not of sufficient size. All the three models trained on NIST DB4 fingerprint and IIIT-D latent fingerprint databases report good accuracy. By only fine-tuning the pre-trained convolutional neural network model, we get the accuracy of 99%, easily out-performing the state-of-the-art.

Comparative analysis of deep convolution neural network models on small scale datasets

Lung cancer remains one of the deadliest cancers worldwide, with survival rates significantly dependent on early detection. Traditional diagnostic methods, while effective, often identify lung cancer in advanced stages, limiting treatment options. Recently, artificial intelligence (AI) and deep learning (DL) models have gained attention for their potential to assist in early lung cancer diagnosis through radiological data, particularly computed tomography (CT) and X-ray imaging. This review examines recent advancements in AI and DL models specifically applied to early lung cancer detection, offering a comprehensive look at model architecture, data preprocessing techniques, and performance metrics. AI models, particularly those using convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown significant promise in improving diagnostic accuracy. In studies published over the past two years, CNN-based models have achieved sensitivity rates upwards of 90% when applied to large datasets, such as the LIDC-IDRI, a publicly available database containing thousands of annotated lung scans. Furthermore, hybrid approaches combining AI models with radiologist expertise have demonstrated reduced false positives, a frequent challenge in automated image analysis. A critical factor in these advancements has been data quality and volume. Large, annotated datasets with high-resolution images have enabled more robust training and validation, essential for refining these models. However, challenges remain, particularly regarding the standardization of data across institutions, variations in scanner quality, and ethical concerns surrounding patient privacy. The review also highlights studies exploring multi-modal approaches, integrating radiological data with clinical records and genetic markers to create more personalized diagnostic tools. These multi-modal methods have the potential to improve predictive accuracy further, though they currently require more extensive validation. Overall, while AI and DL technologies offer transformative potential for early lung cancer detection, widespread implementation depends on continued research in model accuracy, data standardization, and ethical safeguards. This paper concludes by emphasizing the need for collaboration across disciplines—AI researchers, radiologists, and oncologists—to refine these models into reliable tools that can be integrated seamlessly into clinical workflows for timely and accurate lung cancer diagnosis.

Plant diseases compose a great threat to global food security. However, the rapid identification of plant diseases remains challenging and time-consuming. It requires experts to accurately identify if the plant is healthy or not and identify the type of infection. Deep learning techniques have recently been used to identify and diagnose diseased plants from digital images to help automate plant disease diagnosis and help non-experts identify diseased plants. While many deep learning applications have been used to identify diseased plants and aims to increase the detection rate, the limitation of the large parameter size in the models persist. In this paper, an end-to-end deep learning model is developed to identify healthy and unhealthy corn plant leaves while taking into consideration the number of parameters of the model. The proposed model utilizes two pre-trained convolutional neural networks (CNNs), EfficientNetB0, and DenseNet121, to extract deep features from the corn plant images.The deep features extracted from each CNN are then fused using the concatenation technique to produce a more complex feature set from which the model can learn better about the dataset. In this paper, data augmentation techniques were used to add variations to the images in the dataset used to train the model, increasing the variety and number of the images and enabling the model to learn more complex cases of the data. The obtained result of this work is compared with other pre-trained CNN models, namely ResNet152 and InceptionV3, which have a larger number of parameters than the proposed model and require more processing power. The proposed model is able to achieve a classification accuracy of 98.56% which shows the superiority of the proposed model over ResNet152 and InceptionV3 that achieved a classification accuracy of 98.37% and 96.26% respectively.

Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model.

Lung cancer is the most common cause of cancer death around the world with over 1.80 million deaths per year. About 60% of cases have been diagnosed at an advanced stage, and the 5-year survival rate falls between 10% and 20%. Since lung cancer survival is largely determined by the stage of the disease at diagnosis, developing a reliable and effective screening method for early diagnosis has been a long-term goal of lung cancer care. Through effective screening, lung cancer can be diagnosed and treated earlier, resulting in reduced morbidity and mortality. Lung cancer screening would be more accurate and efficient by incorporating an automated intelligent system that matches or surpasses the diagnostic capabilities of human experts. The use of Artificial Intelligence (AI) for lung cancer screening can yield significant benefits. Application of AI in various healthcare settings is currently proving to be a success due to its ability to emulate human cognition in different areas such as analysis, interpretation, and comprehension of complex data sets using complex algorithms and software. By applying AI to imaging diagnostics, radiologists will be less burdened, and screening for lung cancer will be more sensitive, which, in turn, will reduce the morbidity and mortality associated with lung cancer. There has been a significant amount of research using AI to develop tools for detecting and classifying lung cancer using patient data with the goal of improving outcomes. In this article, we provide an overview of the application of AI in lung cancer for improving nodule detection and classification, as well as the challenges that remain to be overcome.A comprehensive search was performed in PubMed, Google Scholar, Web of Science, IEEEXplore, and the Cochrane Database for studies published between 2011 and 2021. The search terms were “lung cancer,” “Artificial Intelligence,” “machine learning,” “deep learning,” “screening,” “detection,” and “classification.” After excluding animal studies, relevant articles reporting AI models for the analysis of chest CT scan images in the case of lung cancer detection were discussed.Based on the results of this study, there is no doubt that AI-implemented neural networks will be the future of lung cancer screening and diagnosis. In CT images, these neural networks offer great promise in detecting lung nodules with a diameter of just 3 mm. Thus, neural networks can detect lung nodules in their earliest stages, thereby improving the survival rate of the patients.In this chapter, we discuss the role of AI, especially neural networks, in lung cancer as it relates to improving screening, diagnosis, and patient’s outcomes. There is a need for future research that will investigate deep neural networks in a clinical and biological context, as well as validate these networks in prospective data.KeywordsArtificial Intelligence (AI)Computer-aided detection (CAD)Convolutional neural networks (CNNs)DiagnosisLung cancerLung cancer detectionMachine learning (ML)Pulmonary nodule

Destructive cyber-attacks and cybercriminals are increasing with the increase in IoT (Internet of Things) devices globally. This ha/s led to the need for increase in security in IoT systems. Innovative and novel techniques are used by the intruders to accomplish malicious goals effectively through cyber-attacks. An Intrusion Detection System (IDS) is used for classification of attacks in IoT networks based on anomaly detection and machine learning techniques. Inefficiency is observed in the conventional machine learning models and intrusion detection techniques as the network technologies are unpredictable. Accurate identification of various anomalies is possible with deep learning models in several research segments. The input data along with its prominent characteristics may be categorized automatically for classification and anomaly detection using convolutional neural networks (CNN). Faster computations are enabled due to the performance efficiency of CNN. For IoT networks, an intrusion detection model based on anomaly detection is designed and developed in this paper. A multiclass classification framework is created initially using a CNN model. Further, 3D CNN is used for implementation of the proposed model. Various intrusion detection datasets from IoT networks are used for validation of the proposed CNN model. Pre-trained multiclass CNN model is used for implementation of multiclass and binary classification based on transfer learning. When compared to the conventional deep learning models, the proposed multiclass and binary classification framework has attained improved F1 score, recall, precision and accuracy.

Comparative performance of four CNN-based deep learning variants in detecting Hispa pest, two fungal diseases, and NPK deficiency symptoms of rice (Oryza sativa)

Lung cancer is a malignant tumor of the lungs that causes uncontrolled cell development in the tissues, and it is the world’s most lethal cancer. The main aim of our study is to predict lung cancer type and its severity using a two-dimensional convolutional neural network (CNN). For achieving the objective, computed tomography scans of different types of lung cancer (adenocarcinoma, large cell carcinoma, squamous cell carcinoma) and healthy lungs were collected. Further, the improvisation of the CT scans was done using the Weiner filter. The improved images were fed into the CNN model. Flask app is used to anticipate the type and the severity of lung cancer for the validated images. The accuracy achieved by the CNN model is above 90%. The accuracy achieved for different classes such as adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and healthy lungs is 90%, 92%, 91%, and 92% respectively. Moreover, other metrics are included to evaluate our model which involves precision, recall-measure scores, and the results are calculated as 0.99, 0.90, and 0.94 respectively. The usage of the flask app in prediction makes our study real-time monitoring. Moreover, this study will help doctors in the diagnosis of lung cancer in a fraction of seconds due to the usage of the flask app.

Convolutional neural networks (CNNs) are an effective technique for hyperspectral image classification. Deep learning models require a large number of labeled and diverse samples to properly train a CNN model. But a training set is often not large enough. Transfer learning can help to overcome the need for training sets. In this paper, six pre-trained CNN models: EfficientNetB0, EfficientNetB7, ResNet50, VGG19, DenseNet121 and DenseNet201 are fine-tuned for hyperspectral image classification. The experiments are carried out on two benchmark images Houston and Kennedy Space Center (KSC). The results show that hyperspectral images can be classified with good accuracy by fine-tuned pre-trained CNN models. As compared to training a model from scratch, fine-tuning takes a small number of epochs. Thus, alleviating the requirement for high-end computing resources. Among the tested models, VGG19 achieves the best accuracy of 95.77% for the KSC image, while EfficientNetB0 performs better than others with 90.79% accuracy for the Houston image.