Pasupa, Kitsuchart

Loading...
Thumbnail Image
Preferred name
Pasupa, Kitsuchart
Main Affiliation
Email
kitsuchart.pa@kmitl.ac.th

Settings

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Convolutional neural networks based focal loss for class imbalance problem: a case study of canine red blood cells morphology classification
    (2023-11-01)
    Pasupa, Kitsuchart
    ;
    Vatathanavaro, Supawit
    ;
    Tungjitnob, Suchat
    Morphologies of red blood cells are normally interpreted by a pathologist. It is time-consuming and laborious. Furthermore, a misclassified red blood cell morphology will lead to false disease diagnosis and improper treatment. Thus, a decent pathologist must truly be an expert in classifying red blood cell morphology. In the past decade, many approaches have been proposed for classifying human red blood cell morphology. However, those approaches have not addressed the class imbalance problem in classification. A class imbalance problem—a problem where the numbers of samples in classes are very different—is one of the problems that can lead to a biased model towards the majority class. Due to the rarity of every type of abnormal blood cell morphology, the data from the collection process are usually imbalanced. In this study, we aimed to solve this problem specifically for classification of dog red blood cell morphology by using a Convolutional Neural Network (CNN)—a well-known deep learning technique—in conjunction with a focal loss function, adept at handling class imbalance problem. The proposed technique was conducted on a well-designed framework: two different CNNs were used to verify the effectiveness of the focal loss function and the optimal hyperparameters were determined by fivefold cross-validation. The experimental results show that both CNNs models augmented with the focal loss function achieved higher F<inf>1</inf> -scores, compared to the models augmented with a conventional cross-entropy loss function that does not address class imbalance problem. In other words, the focal loss function truly enabled the CNNs models to be less biased towards the majority class than the cross-entropy did in the classification task of imbalanced dog red blood cell data.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Improved Identification of Imbalanced Multiple Annotation Intent Labels with a Hybrid BLSTM and CNN Model and Hybrid Loss Function
    (2021-01-01)
    Vatathanavaro, Supawit
    ;
    Pasupa, Kitsuchart
    ;
    Sirirattanajakarin, Sorratat
    ;
    Suntisrivaraporn, Boontawee
    Payment or fund transfer transactions can be annotated by users when they are made through a mobile banking app, for example, SCB Easy app—a mobile banking app by Siam Commercial Bank—allows users to annotate transactions with 40 character texts. The AI<sup>2</sup> framework was used to identify user intentions with the transactions, so that the bank can offer the right product to the right customer at the right time. The framework employed Long Short-Term Memory (LSTM). Commonly, one annotated sample can be interpreted as representing multiple intents, thus we had a multiple label classification problem. However, the original model did not consider the class imbalance, that caused the model to bias toward the majority class. We introduced a new hybrid Bidirectional LSTM and Convolutional Neural Network model in conjunction with a new hybrid loss function to tackle the imbalance. Our model with hybrid loss function performed better than the AI<sup>2</sup> framework with a 4.5% improvement in F<inf>1</inf> -score. Moreover, our hybrid loss function enabled the model to classify minority classes better, when the imbalance ratio became higher, compared with a conventional cross-entropy loss function. In other words, our hybrid loss function made the model to be more efficient in real-world multiple label imbalance problem.