Convolutional neural network and its pretrained models for image classification and object detection: A survey

Jena, Biswajit; Nayak, Gopal Krishna; Saxena, Sanjay

doi:10.1002/cpe.6767

Cited by 38 publications

(6 citation statements)

References 182 publications

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“…In FEAPAS, we used InceptionV3 as the building block of our classifier. The choice was made because InceptionV3 can provide accurate and efficient results in the absence of a very large amount of training data [29]. The InceptionV3 architecture is inspired by the Network in Network (NIN) model, which employs 1 × 1 filters for feature extraction.…”

Section: Feapasmentioning

confidence: 99%

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SAFEPA: An Expandable Multi-Pose Facial Expressions Pain Assessment Method

Alghamdi

Alaghband

2023

Applied Sciences

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Accurately assessing the intensity of pain from facial expressions captured in videos is crucial for effective pain management and critical for a wide range of healthcare applications. However, in uncontrolled environments, detecting facial expressions from full left and right profiles remains a significant challenge, and even the most advanced models for recognizing pain levels based on facial expressions can suffer from declining performance. In this study, we present a novel model designed to overcome the challenges posed by full left and right profiles—Sparse Autoencoders for Facial Expressions-based Pain Assessment (SAFEPA). Our model utilizes Sparse Autoencoders (SAE) to reconstruct the upper part of the face from the input image, and feeds both the original image and the reconstructed upper face into two pre-trained concurrent and coupled Convolutional Neural Networks (CNNs). This approach gives more weight to the upper part of the face, resulting in superior recognition performance. Moreover, SAFEPA’s design leverages CNNs’ strengths while also accommodating variations in head poses, thus eliminating the need for face detection and upper-face extraction preprocessing steps needed in other models. SAFEPA achieves high accuracy in recognizing four levels of pain on the widely used UNBC-McMaster shoulder pain expression archive dataset. SAFEPA is extended for facial expression recognition, where we show it to outperform state-of-the-art models in recognizing seven facial expressions viewed from five different angles, including the challenging full left and right profiles, on the Karolinska Directed Emotional Faces (KDEF) dataset. Furthermore, the SAFEPA system is capable of processing BioVid Heat Pain datasets with an average processing time of 17.82 s per video (5 s in length), while maintaining a competitive accuracy compared to other state-of-the-art pain detection systems. This experiment demonstrates its applicability in real-life scenarios for monitoring systems. With SAFEPA, we have opened new possibilities for accurate pain assessment, even in challenging situations with varying head poses.

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Section: Feapasmentioning

confidence: 99%

“…The mean squared error (MSE) is commonly used as a loss function in the autoencoder. Assuming M equals the number of the observations in the training dataset, then MSE focuses on the distance between compressed data and reconstructed data as shown in Equation ( 5) [29,30].…”

Section: Sparse Autoencoders Saementioning

confidence: 99%

SAFEPA: An Expandable Multi-Pose Facial Expressions Pain Assessment Method

Alghamdi

Alaghband

2023

Applied Sciences

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“…According to studies, classification techniques, including machine learning (ML) and deep learning (DL) algorithms, play a vital role (Kussul et al, 2017;Jagannathan and Divya, 2021;Digra et al, 2022;Swetanisha et al, 2022;Wang et al, 2022;Azedou et al, 2023;Boonpook et al, 2023;Ebenezer and Manohar, 2023). DL models, demonstrated by Convolutional Neural Networks (CNN) (Jena et al, 2022) and Recurrent Neural Networks (RNN) (Gaafar et al, 2022), have gained importance due to their ability to extract and classify features efficiently (Swetanisha et al, 2022). Spatial data, remote sensing, and machine learning analysis have a wide range of applications, including urban planning, agriculture, resource management, mineralogy for the environmental management and planning (Hussein et al, 2023;Iqbal et al, 2023;Kanakala and Reddy, 2023) and have been used for natural language processing (NLP) (Agga et al, 2022;Vankdothu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Nigar,

Li,

Jat Baloch

et al. 2024

Front. Environ. Sci.

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Classifying land use and land cover (LULC) is essential for various environmental monitoring and geospatial analysis applications. This research focuses on land classification in District Sukkur, Pakistan, employing the comparison between machine and deep learning models. Three satellite indices, namely, NDVI, MNDWI, and NDBI, were derived from Landsat-8 data and utilized to classify four primary categories: Built-up Area, Water Bodies, Barren Land, and Vegetation. The main objective of this study is to evaluate and compare the effectiveness of comparison of machine and deep learning models. The machine learning models including Random Forest achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.90. It accurately classified 2.7% of the area as Built-up Area, 1.9% as Water Bodies, 54.8% as Barren Land, and 40.4% as Vegetation. While slightly less accurate, Decision Tree model provided reliable classifications. Deep learning models showed significant accuracy, of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The CNN model achieved an impressive overall accuracy of 97.3%, excelling in classifying Water Bodies with User and Producer Accuracy exceeding 99%. The RNN model, with an overall accuracy of 96.2%, demonstrated strong performance in categorizing Vegetation. These findings offer valuable insights into the potential applications of machine learning and deep learning models for perfect land classifications, with implications for environmental monitoring management and geospatial analysis. The rigorous validation and comparative analysis of these models contribute to advancing remote sensing techniques and their utilization in land classification tasks. This research presents a significant contribution to the field and underscores the importance of precise land classification in the context of sustainable land management and environmental conservation.

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“…More recently, deep learning (DL) methods came into existence [ 31 , 32 ] and have shown several medical imaging applications such as in brain cancer [ 33 ], carotid wall segmentation [ 34 , 35 ], COVID lesion detection, lung segmentation [ 36 , 37 ], and coronary/carotid plaque classification [ 38 , 39 ]. These DL techniques are certainly better than ML, but they are pretty challenging due to the cost of training time.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Jena

Saxena

Nayak

et al. 2022

Cancers

Self Cite

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Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of “radiomics and genomics” has been considered under the umbrella of “radiogenomics”. Furthermore, AI in a radiogenomics’ environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor’s characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

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Convolutional neural network and its pretrained models for image classification and object detection: A survey

Cited by 38 publications

References 182 publications

SAFEPA: An Expandable Multi-Pose Facial Expressions Pain Assessment Method

SAFEPA: An Expandable Multi-Pose Facial Expressions Pain Assessment Method

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

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