Senthil Kumar

Lung nodule detectionis necessary for lung cancer treatment, which is crucial for treating the patients. However, the current dataset comprises only a limited amount of lung Computed Tomography (CT) images, with a significant imbalance between non-nodule and nodule samples. This disparity reduces the performance of neural networks and makes training more difficult. Early detection of lung tumor is crucial for identifying patients with ahigh chance of effective treatment, and this relies heavily on the preciserecognition of malignant lung nodules in CT scans. Recently, Deep Neural Network (DNN)techniques have been successfully employed to address various computer vision challenges, demonstrating their potential in this domain. Yet, low training values present significant challenges in the diagnosis of malignant nodules. The size and shape of a lump are crucial factors in determining malignancy in lung cancer. Therefore, it is important to address limitations of conventional techniques through the application of deep learning strategies. The developed lung nodule classification framework contains three main stages: image collection, segmentation, and classification. Initially, a substantial number of CT images used for validation are gathered from publicly available sources. These images are processed through a segmentation module, where effective segmentation is performed using a 3D Trans-DenseUnet++ (3D-TDUnet++) model. This segmentation process segregates the lung nodules from nearby tissues and also ignores inappropriate background structures, which helps the suggested approachfocus only on the region of interest. Also, it enhances the feature extraction process and reduces noise from images, which strengthens the classification accuracy and overall reliability of the developed method. After segmentation, the attained segmented images is further given into the classification phase by using an Adaptive DenseNet combined with a Long Short-Term Memory (LSTM) layer (ADNet-LSTM).This synergy of the classification-based deep learning model empowers an automated differentiation between malignant and benign lung nodules, which yields an accurate and efficient clinical decision-making process. It efficiently reduces the manual effort, improves the early detection process and also enhances the detection consistency. Additionally, the parameters of an ADNet-LSTM are optimized with the help of Intensified Fitness-based Red-Tailed Hawk Algorithm (IF-RTHA). Furthermore, some experimental validations are performed over the developed models with several performance metrics to ensure model’s reliability. The accuracy of the IF-RTH-ADNet-LSTM model is 94.98% higher than the existing works, such as RAN, Densenet, LSTM, and ADNet-DenseNet, as 90.21%, 92.38%, 91.68%, and 92.87% using the lung nodule dataset. Thus, it is revealed that the offered lung nodule classification framework performed well for evaluating the malignancy risk of lung nodules found on CT images and also it has the efficiency to provide better decisions for clinicians.

COIMBATORE: Devotees intending to climb the Velliangiri Hills may soon be permitted only through an online booking system. The forest department has drafted a p

COIMBATORE: Manombolly Forest Range officials have initiated a study on a three-hectare land area in Valparai plateau, where tea was cultivated, to assess the g

COIBATORE: A wild elephant, fondly called ‘Chillikomban' by the people of Tamil Nadu and Kerala, accidentally fell into the contour canal near Aliyar in the Pollachi forest range on Saturday evening and died after being washed away in the strong currents.

COIMBATORE: The mother of the near Sulur in Coimbatore district, alleged that her husband's family cremated her daughter's body without her consent and without letting her see it.

NILGIRIS: Officials of the Animal Husbandry Department in Nilgiris district were pleasantly surprised by the simplicity of Animal Husbandry Minister S Kamali during her first visit to the district after assuming office.

Author(s) Mr. Rajadurai S Dr. S. Senthil Kumar Country India Abstract Background

COIMBATORE: More than 21 months after promoting 'Foresters' as Forest Range Officers (FRO), the state forest department issued an order on Tuesday

Two-line hybrid rice breeding based on thermo-sensitive genetic male sterility (TGMS) provides an efficient alternative to the conventional three-line system. The present study evaluated trait variability and selection potential in early segregating F2 and F3 populations derived from five TGMS × non-TGMS crosses to support effective TGMS-based breeding under contrasting environments. Analysis of variance revealed significant variation among F2 segregants, F3 progeny lines and parents for all morphological traits. Spikelet sterility showed a significant positive association with pollen sterility and stigma exertion rate in both generations. High heritability coupled with high genetic advance as a percentage of mean was observed for number of productive tillers per plant, stigma breadth and anther length, suggesting the possible involvement of additive gene effects, indicating traits amenable to effective early-generation selection. Hierarchical clustering separated progenies and parents into distinct groups, reflecting effective recombination. Significant intergenerational correlation and parent–progeny regression indicated that early-generation selection appears promising, but requires further validation in advanced generation, spikelet sterility is highly influenced by environmental factors. Thus, it is suggested the effectiveness of single plant selection would be effective in early generations for selection of superior genotypes. Overall, pollen sterility, stigma length, stigma breadth, anther length, stigma exertion rate, and number of productive tillers per plant were identified as key selection criteria for the development and selection of potential TGMS lines in early generations. We have selected 50 promising progeny lines that meet our selection criteria and represent promising candidate progeny lines combining desirable TGMS expression and agronomic traits, which require further validation in advanced generations. The selected progeny lines provide a strong foundation for advancing TGMS breeding by integrating morphological selection with molecular tools.

BackgroundHand grip strength (HGS) is a vital measure of muscular function and serves a pivotal role in the identification of functional impairments, as well as in evaluating the effectiveness of therapeutic interventions in individuals with hand or upper extremity injuries undergoing rehabilitation. HGS represents a simple, objective, and reliable indicator of physical status, muscular performance, cardiovascular fitness, and functional disability.The primary objective of the study was to estimate HGS using a grip force transducer and to establish normative reference data for a healthy population from central India. Secondary exploratory objectives included examining the relationships of HGS with anthropometric and physiological variables, such as age, body mass index (BMI), heart rate (HR), and electromyography (EMG) activity, to provide additional physiological context for interpreting grip strength performance.MethodsThe study was descriptive and cross-sectional in design and was conducted in the sports physiology laboratory of the Department of Physiology at a rural central Indian medical institute. A total of 400 volunteers aged 17-65 years were selected according to the inclusion criteria. HGS was assessed using a grip force transducer. Surface EMG measurements were obtained through disposable silver-silver chloride electrodes. Data acquisition was performed using the PowerLab system (ADInstruments Pty Ltd., Dunedin, New Zealand), and the analysis was carried out using LabChart Pro software (ADInstruments Pty Ltd., Dunedin, New Zealand).ResultsOf the 400 participants (220 men and 180 women), the mean age was 34 ± 11 years. The mean HGS was significantly lower in females than in their male counterparts, as revealed by the unpaired t-test. There was also a significant inverse correlation between mean HGS and age in males (r = -0.30) and females (r = -0.36). Although HR was significantly positively correlated with mean HGS, the correlation coefficients were relatively weak (for males, r = 0.23, and for females, r = 0.18), indicating that HR explains only a small proportion of the variability in HGS. The root mean square values of EMG demonstrated a statistically non-significant, weak positive correlation with mean HGS.ConclusionThe present research has established age-specific normative values of HGS, estimated using a grip force transducer, for the healthy population of central India. A statistically significant difference in grip strength was noted between the sexes, with men exhibiting greater grip strength than women. Age and HGS were also found to be inversely related, whereas a statistically significant positive correlation was observed between HR and HGS.