FEW-SHOT LEARNING: REVIEW OF APPLICATIONS IN HEALTH AND AGRICULTURE

Abstract

Few-Shot Learning (FSL) is an innovative machine learning method that aims to achieve high accuracy with limited data. In this study, we focus on the applications of FSL in healthcare and agriculture, where data collection is difficult and costly. In the health sector, FSL provides high accuracy with little data in skin cancer diagnosis and eye disease diagnosis. In the agriculture sector, it is used in the early diagnosis of plant diseases to increase crop productivity. The study examines FSL's meta-learning, transfer learning and similarity-based techniques to assess its advantages over data constraints and its potential for future applications.

Keywords

• Few-Shot Learning
• Meta-learning
• Transfer learning
• Similarity-based learning
• Data limitation
• Medical imaging
• Plant disease diagnosis
• Skin cancer diagnosis
• Agriculture practices

AZ ATIŞLI ÖĞRENME: SAĞLIK VE TARIM ALANINDA UYGULAMALARIN İNCELENMESİ

Abstract

Few-Shot Learning (FSL), sınırlı veriyle yüksek doğruluk elde etmeyi hedefleyen yenilikçi bir makine öğrenimi yöntemidir. Bu çalışmada, FSL'nin veri toplamanın zor ve maliyetli olduğu sağlık ve tarım alanındaki uygulamaları ele alınmaktadır. Sağlık sektöründe cilt kanseri teşhisi ve göz hastalıklarının tanısında FSL, az veri ile yüksek doğruluk sağlamaktadır. Tarım sektöründe ise bitki hastalıklarının erken teşhisinde kullanılarak ürün verimliliğini artırmaktadır. Çalışma, FSL’nin meta-öğrenme, aktarım öğrenmesi ve benzerlik tabanlı tekniklerini inceleyerek veri kısıtlılığına karşı sunduğu avantajları ve gelecekteki uygulama potansiyelini değerlendirmektedir.

Keywords

• Few-Shot Learning
• Meta-öğrenme
• Aktarım öğrenmesi
• Benzerlik tabanlı öğrenme
• Veri kısıtlılığı
• Tıbbi görüntüleme
• Bitki hastalığı tanısı
• Cilt kanseri teşhisi
• Tarım uygulamaları
• Sağlık uygulamaları

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