Deep learning-based anomaly detection in images has recently been considered a popular research area with numerous applications worldwide. The main aim of anomaly detection (i.e., Outlier detection), is to identify data instances that deviate considerably from the majority of data instances. This paper offers a comprehensive analysis of previous works that have been proposed in the area of anomaly detection in images through deep learning generally and in the medical field specifically. Twenty studies were reviewed, and the literature selection methodology was defined based on four phases: keyword filter, publish filter, year filter, and abstract filter. In this review, we highlight the differences among the studies included by considering the following factors: methodology, dataset, preprocessing, results and limitations. Besides, we illustrate the various challenges and potential future directions relevant to anomaly detection in images.
Background: Mango is known as the king of fruits. It has several varieties however, some are more popular in the country including Sindhri. Each variety has its significance when used in different food products. The pulp of mango is used for developing jams, beverages, chutney (dips) etc., and sometimes preserved with some additives to be used for the production of mango leather. In general, the application of drying techniques to fruit puree yields a stable shelf-life product called fruit leather, which is of soft rubbery texture with a sweet taste and especially dehydrated. Objectives: To analyze the effect of various drying techniques (i.e. sun drying, oven drying, and dehydration) on the organoleptic quality (i.e. quality that affects how a consumer experiences the food via their senses e.g. look, taste, smell, and touch) of mango leather. Methodology: A drying experiment was performed like sun drying, hot air oven drying, and commercial dehydrator to determine the effect of drying times on the quality of mango leathers using these techniques. The effect of the storage period was also studied for the quality of mango leathers. The dried mango leathers were sensory analyzed by the trained panelists. Results: A minimum drying time of 8-10hours was achieved in a commercial dehydrator for mango fruit leather at 70°C. It was also observed that despite the longer dehydration time, the mango leathers produced remained equally acceptable compared to other dehydration techniques. The mango leathers dried in dehydrater at 70°C ± 2°C gave the highest score of color (6.20), texture (6.13), flavour/taste (5.88), appearance (5.88), and overall acceptability (5.85), while mango leather dried in an oven at 70°C ± 2°C has recorded a score of color (5.93), texture (5.87), flavour/taste (5.77), appearance (5.53), and overall acceptability (5.70). The storage of mango leathers had also a significant effect (p < 0.05) in the storage period of mango leathers as compared to control. A non-significant difference was also observed in all the organoleptic parameters after 10-12 weeks of storage. Conclusion: It is obtained from the current study that for the production of mango leather, the commercial dehydrator based drying techniques results in acceptable sensory characteristics as well as longer storage period by comparing other drying techniques, as it has a controlled environment.
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