Multi Loss ( BCE Loss + Focal Loss ) + Dice Loss

Our proposed loss function is a combination of BCE Loss, Focal Loss, and Dice loss. Each one of them contributes individually to improve performance further details of loss functions are mentioned below,

(1) BCE Loss calculates probabilities and compares each actual class output with predicted probabilities which can be either 0 or 1, it is based on Bernoulli distribution loss, it is mostly used when there are only two classes are available in our case there are exactly two classes are available one is background and other is foreground. In a proposed method it is used for pixel-level classification.

(2) Focal Loss is a variant of BCE, it enables the model to focus on learning hard examples by decreasing the wights of easy examples it works well when the data is highly imbalanced.

(3) Dice Loss is inspired by the Dice Coefficient Score which is an evaluation metric used to evaluate the results of image segmentation tasks. Dice Coefficient is convex in nature so it has been changed, so it can be more traceable. It is used to calculate the similarity between two images, Dice Loss represent as

We proposed a Loss function which is a combination of all three above mention loss functions to benefit from all, BCE is used for pixel-wise classification, Focal Loss is used for learning hard examples, we use 0.25 as the value for alpha and 2.0 as the value of gamma. Dice Loss is used for learning better boundary representation, our proposed loss function represent as \begin{equation} Loss = \left( BCE Loss + Focal Loss \right) + Dice Loss \end{equation}