Automatic detection and classification of disease in citrus fruit and leaves using a customized CNN based model / Detección y clasificación automática de enfermedades en frutas y hojas de cítricos utilizando un modelo basado en CNN personalizado

India's commercial advancement and development depend heavily on agriculture. A common fruit grown in tropical settings is citrus. A professional judgment is required while analyzing an illness because different diseases have slight variati ons in their symptoms. In order to recognize and classify diseases in citrus fruits and leaves, a customized CNN - based approach that links CNN with LSTM was developed in this research. By using a CNN - based method, it is possible to automatically differenti ate from healthier fruits and leaves and those that have diseases such fruit blight, fruit greening, fruit scab, and melanoses. In terms of performance, the proposed approach achieves 96% accuracy, 98% sensitivity, 96% Recall, and an F1 - score of 92% for ci trus fruit and leave identification and classification and the proposed method was compared with KNN, SVM, and CNN and concluded that the proposed CNN - based model is more accurate and effective at identifying illnesses in citrus fruits and leaves.

El avance y desarrollo comercial de India dependen en gran medida de la agricultura. Un tipo de fruta comunmente cultivada en en tornos tropicales es el cítrico. Se requiere un juicio profesional al analizar una enfermedad porque diferentes enfermedades tienen ligeras variaciones en sus síntomas. Para reconocer y clasificar enfermedades en frutas y hojas de cítricos, se desarrolló e n esta investigación un enfoque personalizado basado en CNN que vincula CNN con LSTM. Al utilizar un método basado en CNN, es posible diferenciar automáticamente entre frutas y hojas más saludables y aquellas que tienen enfermedades como la plaga de frutas , el verdor de frutas, la sarna de frutas y las melanosis. En términos de desempeño, el enfoque propuesto alcanza una precisión del 96%, una sensibilidad del 98%, una recuperación del 96% y una puntuación F1 del 92% para la identificación y clasificación d e frutas y hojas de cítricos, y el método propuesto se comparó con KNN, SVM y CNN y se concluyó que el modelo basado en CNN propuesto es más preciso y efectivo para identificar enfermedades en frutas y hojas de cítricos.

Effectiveness of implementation of formative assessments as a part of competency-based medical education on summative assessment: A pilot study

Background: Assessment is an essential part of each and every education, which represents the learning of a student. If the assessments are performed regularly, it inspires active study habits and inevitably enhance learning. Aim and Objectives: Formative assessments, which are performed regularly, increase the effectiveness of the learning. Hence, we aimed to observe the effectiveness of spaced formative assessments on the performance of students in summative assessments. Materials and Methods: We selected 250 first-year medical students from the 21 to 22 batch. The students were categorized into three groups on the basis of formative assessment performance. Group I did not appear in the formative assessment, Group II scored <50% in the formative assessment, and Group III scored more than 50% in the formative assessment. Multiple comparisons of scores of summative assessments between different groups were done using ANOVA. Scores of formative assessments and summative assessment were correlated using Pearson correlation. Results: We got a statistically significant difference (P < 0.05) in mean summative assessment scores in different groups. Again, the analysis showed formative assessments had a significant (P < 0.05) relationship with summative assessment performance. Conclusion: The performance of formative assessments is predictive of summative examination scores. Academically poor medical students will be benefited from formative assessments.

What is the Role of Epidural Injections in the Treatment of Lumbar Discogenic Pain: A Systematic Review of Comparative Analysis with Fusion

BACKGROUND: Lumbar discogenic pain without pain mediated by a disc herniation, facet joints, or the sacroiliac joints, is common and often results in chronic, persistent pain and disability. After conservative treatment failure, injection therapy, such as an epidural injection, is frequently the next step considered in managing discogenic pain. The objective of this systematic review is to determine the efficacy of lumbar epidural injections in managing discogenic pain without radiculopathy, and compare this approach to lumbar fusion or disc arthroplasty surgery. METHODS: A systematic review of randomized trials published from 1966 through October 2014 of all types of epidural injections and lumbar fusion or disc arthroplasty in managing lumbar discogenic pain was performed with methodological quality assessment and grading of evidence. The level of evidence was based on the grading of evidence criteria which, was conducted using 5 levels of evidence ranging from levels I to V. RESULTS: Based on a qualitative assessment of the evidence for both approaches, there is Level II evidence for epidural injections, either caudal or lumbar interlaminar. CONCLUSIONS: The available evidence suggests fluoroscopically directed epidural injections provide long-term improvement in back and lower extremity pain for patients with lumbar discogenic pain. There is also limited evidence showing the potential effectiveness of surgical interventions compared to nonsurgical treatments.