Abstract Plant diseases cause significant agricultural losses, demanding accurate detection methods. Traditional approaches relying on expert knowledge may be biased, but advancements in computing, particularly deep learning, offer non-experts effective tools. This study focuses on fine-tuning cutting-edge pre-trained CNN and vision transformer models to classify grape leaves and diagnose grape leaf diseases through digital images. Our research examined a PlantVillage dataset, which comprises 4062 leaf images distributed across four categories. Additionally, we utilized the Grapevine dataset, consisting of 500 leaf images. This dataset is organized into five distinct groups, with each group containing 100 images corresponding to one of the five grape types. The PlantVillage dataset focuses on four classes related to grape diseases, namely Black Rot, Leaf Blight, Healthy, and Esca leaves. On the other hand, the Grapevine dataset includes five classes for leaf recognition, specifically Ak, Alaidris, Buzgulu, Dimnit, and Nazli. In experiments with 14 CNN and 17 vision transformer models, deep learning demonstrated high accuracy in distinguishing grape diseases and recognizing leaves. Notably, four models achieved 100% accuracy on PlantVillage and Grapevine datasets, with Swinv2-Base standing out. This approach holds promise for enhancing crop productivity through early disease detection and providing insights into grape variety characterization in agriculture. 

Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases

Detecting anomalies in videos presents a significant challenge in the field of video surveillance. The primary goal is identifying and detecting uncommon actions or events within a video sequence. The difficulty arises from the limited availability of video frames depicting anomalies and the ambiguous definition of anomaly. Based on extensive applications of Generative Adversarial Networks (GANs), which consist of a generator and a discriminator network, we propose an Attention-guided Generator with Dual Discriminator GAN (A2D-GAN) for real-time video anomaly detection (VAD). The generator network uses an encoder–decoder architecture with a multi-stage self-attention added to the encoder and multi-stage channel attention added to the decoder. The framework uses adversarial learning from noise and video frame reconstruction to enhance the generalization of the generator network. Also, of the dual discriminator in A2D-GAN, one discriminates between the reconstructed video frame and the real video frame, while the other discriminates between the reconstructed noise and the real noise. Exhaustive experiments and ablation studies on four benchmark video anomaly datasets, namely UCSD Peds, CUHK Avenue, ShanghaiTech, and Subway, demonstrate the effectiveness of the proposed A2D-GAN compared to other state-of-the-art methods. The proposed A2D-GAN model is robust and can detect anomalies in videos in real-time. The source code to replicate the results of the proposed A2D-GAN model is available at https://github.com/Rituraj-ksi/A2D-GAN. 

Attention-guided generator with dual discriminator GAN for real-time video anomaly detection

In recent years, sentiment analysis has emerged as a prominent area of research within the field of natural language processing. Particularly, aspect-level sentiment classification has gained significant attention for its focus on discerning and analyzing sentiment expressed towards specific aspects within sentences. Existing methods primarily rely on extracting keywords from sentence contexts to determine sentiment polarity, yielding satisfactory results. However, a notable limitation of these approaches is their inability to consider the crucial information contained within key phrases in sentences, which plays a vital role in sentiment analysis.To address this limitation, we propose a novel deformable convolutional network model designed to leverage the power of phrases for aspect-level sentiment analysis. By utilizing deformable convolutions with adaptive receptive fields, our model effectively extracts phrase representations at various contextual distances. Furthermore, a cross-correlation attention mechanism is incorporated to capture interdependencies between phrases and words in the context. To evaluate the effectiveness of our approach, we conduct comprehensive evaluations across widely used datasets, demonstrating the promising performance of our model in enhancing sentiment classification tasks. Our model outperforms the model based on CNN, which also leverages phrase extraction, by improving accuracy by 1.71%, 2.5%, and 1.89%, respectively, on the Laptop, Restaurant, and Twitter datasets. Additionally, it surpasses the performance of the latest models. 

Base on contextual phrases with cross-correlation attention for aspect-level sentiment analysis

Deep Learning-Based Automated Emotion Recognition Using Multi modal Physiological Signals and Time-Frequency Methods

Multiscale information enhanced spatial-temporal graph convolutional network for multivariate traffic flow forecasting via magnifying perceptual scope

Aim: Endometrial carcinoma (EC) data from India are very sparse. We did a retrospective analysis of our patients registered at this peripheral cancer center based in rural Punjab and studied their outcome. Materials and Methods: Ninety-eight Stage I and II EC patients with endometroid histology registered at our institute from January 2015 to April 2020 were studied for demography, histopathology, treatment received, and outcomes. FIGO 2009 staging and new European Society for Medical Oncology (ESMO) risk group classification was used. Results: Our patients had a median age of 60 years (range 32–93 years). There were 39 (39.8%), 41 (42.0%), 4 (4.1%), 12 (12.2%) patients in the low risk, intermediate risk (IR), high intermediate risk, and high risk groups, respectively, as per new ESMO risk classification. Two (2.0%) patients had incomplete information to assign them to a particular risk group. Fifty (46.7%) patients underwent complete surgical staging and 54 (50.5%) patients received adjuvant RT. With a median follow-up of 27.0 months, there were 1 locoregional and 2 distant recurrences. There were 8 deaths in total. Three-year overall survival for the entire group is 90.6%. Conclusions: The risk group determines adjuvant treatment in endometrial cancer. Patients operated at dedicated cancer center tend to have better surgical staging and thus better outcome because of better risk stratification and grouping for adjuvant therapy. IR histology was more common in our group of patients, which is variable as compared to available literature.

Early endometrial carcinoma: Experience and outcomes

Patients with head and neck cancer were treated with either 70 Gy in 35 fractions (Arm A) or 66 Gy in 30 fractions (Arm B).

Recurrence pattern with respect to two different dose fractionations in patients with locally advanced head and neck cancer treated with chemoradiation using image‐guided volumetric arc therapy

Authors' reply to Bhattacharyya et al., Thammineedi et al., and Arun

Background: In patients with locally advanced head-and-neck squamous cell carcinoma (HNSCC), chemoradiotherapy improves outcomes. Radiotherapy commonly causes mucositis, which can significantly impede treatment and reduce the patient's quality of life. Patients with severe mucocutaneous toxicity will show significant changes in thermal intensity early on, when compared to identically treated counterparts without toxicity. Objective: We aimed to assess the accuracy of the automated computer-aided deep learning approach in predicting the occurrence of oral mucositis in patients with HNSCC undergoing radiotherapy alone or with concurrent chemotherapy. Materials and Methods: This prospective observational study was conducted over four weeks in September 2021 in the Department of Radiotherapy at the Homi Bhabha Cancer Hospital, Sangrur (Punjab, India). We enrolled patients with HNSCC who were planned for radical intent radiotherapy, with or without concurrent chemotherapy. Using an automated deep learning technique, we analyzed the images taken with a FLIR-E60 thermal camera on the same day that patients received radiotherapy, with or without chemotherapy. Thermal images were binarily classified into two grades, that is, Grade 0 (absence of mucositis) and Grade I (asymptomatic or mild symptoms of mucositis). The dataset was split into training and testing cohorts, with a split ratio of 0.8. Accuracy was calculated as the ratio of correct predicted or classified instances to the total number of instances in the dataset. Accuracy was categorized as testing accuracy and training accuracy. Results: A total of 386 thermal images from 50 patients were acquired. Of these, 308 images (79.8%) were used for the training set and 78 (20.2%) for the testing set. There were 206 images (53.4%) with Grade 0 mucositis and 180 (46.6%) with Grade I. There was a significant difference in the thermal profile of patients with Grade 0 and Grade I images; P = 0.01. The model achieved promising results with 100% training accuracy and 82% testing accuracy. This led to a significant improvement in the false-negative rate of the proposed model, indicating improved performance. Conclusion: The deep learning approach-based analysis of thermal images can be a useful technique for predicting oral mucositis at an early stage in treatment, thus helping in intensifying supportive care. The model has been tested on a diverse dataset, and its performance in terms of accuracy validates the proposed model.

Artificial intelligence-based prediction of oral mucositis in patients with head-and-neck cancer: A prospective observational study utilizing a thermographic approach

We thank Kapoor and Mahajan,[1] Fazal et al.,[2] and Gupta and Rangarajan[3] for their keen interest, valuable appreciation, and insightful comments on our article, “Artificial intelligence-based prediction of oral mucositis in patients with head-and-neck cancer: A prospective observational study utilizing a thermographic approach.”[4] We agree with Fazal et al.[1] that, when assessing oral mucositis, it is imperative to give due consideration to the patient’s clinical history and conduct a thorough physical examination. These aspects hold paramount importance in the evaluation process. At the Homi Bhabha Cancer Hospital, Tata Memorial Center, Sangrur, India, we maintain patient records both in the electronic medical record (EMR) system and physical files, while also conducting regular patient examinations. We completely acknowledge the validity of the comment by Fazal et al.[1] that doctors invest significant time in evaluating medical images, and the automation of thermal image processing with the help of artificial intelligence would reduce computational time.[5,6] In the future, more efforts should be made to improve the computational algorithms for larger datasets. We agree with the comments by Kapoor and Mahajan[2] that radiation-induced mucositis takes a minimum of 5–14 days to evolve, and thus, the data acquisition must be done within that specific time slot. Thermal imaging of patients with head-and-neck cancer was conducted over a four-week period as part of a preliminary (pilot) study. Our study[4] was cross-sectional, but thermal data were acquired every week; hence, in many cases, the thermal data were possibly from the same patient in consecutive weeks, but we did not document the data (details) on a weekly basis, which could have provided better clarity to the thermal data. Obtaining real-time data is an extremely time-consuming process, given the concurrent focus on uninterrupted treatment for patients with head-and-neck cancer during the data acquisition phase. The aim of our study[4] was to check the predictability of artificial intelligence-based thermal imaging for oral mucositis. We did not document the clinical aspects. To clarify, we included all cases that received a curative radical radiation dose of 70 Gy; we did not include any patients who received palliative radiotherapy. We agreed with the observation of Gupta and Rangarajan[3] that a larger sample size could have made the deep learning method more sensitive.[7] Real-time thermal data acquisition is a time-consuming process, and data acquisition is still ongoing. In the future, more efforts will be made to improve the computational algorithm on larger thermal datasets that contain images from patients with all grades of mucositis. We thank Gupta and Rangarajan[3] for their recommendations. We will go through the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) checklists and ensure that we incorporate them in future studies.[8,9] We express our heartfelt gratitude for their valuable suggestions, which we hold in the highest regard. The inputs are immensely appreciated, and we are deeply thankful for the contributions. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. 

Authors' reply to Kapoor and Mahajan, Fazal et al., and Gupta and Rangarajan

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