This work presents a solution adaptive learning approach based on the core of the CNN model. The proposed method automatically updates the recognition model according to online training dataset accumulated directly from the system and retraining recognition model. The data updating task focuses on data samples that are unlike previous trained ones. The purpose of this solution is to upgrade the model to a new one more adaptive, expecting to reach higher accuracy. In the adaptive learning approach, the recognition system is capable of self-learning and complementing data, without experts needed for data labeling or training. The proposed solution includes 5 main phases: (1) Detect and recognize low confident objects; (2) Track objects in n frames in future progress to make sure whether they are interesting objects or not. (3) In case of objects that are recognized with high confidence: labeling (same class of object) for the corresponding data samples to be recognized with low confidence scores which were tracked in the previous process. In case of objects determined not to be of interesting objects, the samples are labeled as Negative for all previous samples, which were tracked in n previous frames; (4) Initialize a training dataset based on a selective combination of previously trained data and the new data. (5) Retrain and update the model if it results in higher accuracy. We have conducted experiments to compare results of the proposed model - PDnet and some state-of-the-art methods such as AlexNet and Vgg. The experimental results demonstrate that the proposed method provides the higher accuracy when the model is self-learned over time. On the other hand, our adaptive learning is applicable to the traditional recognition models such as AlexNet and Vgg model for improving accuracy.

In this work, we investigated an optimization solution for learning hyperparameters of adaptive learning systems for improving object recognition accuracy. The proposed method was developed from a framework searching a set of learning hyperparameters based on the evaluation of the previous CNN model with the collected dataset during the movement of advanced driver assistance systems equipment. The proposed approach consists of some major steps in a loop of adaptive learning system, such as (1) training an initial recognition model, (2) locating and receiving image data of different cases of the object during ADAS movement based on object tracking process, (3) finding optimal hyperparameters on the found dataset based on the previous recognition model, and (4) using the trained recognition model to update the current recognition model. The experimental results proved that the trained recognition model was capable of being more intelligent and displayed more diverse recognition than the previous model. The updated task for the recognition model was continuously repeated throughout the advanced driver assistance systems life. This approach supports and enables the recognition system to be self-adaptive and more intelligent in real life settings without manually processing.

Skin cancer is one of the most common cancers in the world. However, the disease is curable if detected in the beginning stage. Early detection of malignant lesions through accurate techniques and innovative technologies has a significant impact on reducing skin cancer mortality rates. Recently, artificial intelligence has come to the forefront to facilitate skin cancer diagnosis based on medical images. Many deep learning models have been studied and developed, but the imbalance of performance among classes in the multi-class classification is still a challenging problem.

In this work, we proposed a hybrid approach for handling class imbalance of skin-disease classification. The proposed method combines the data level method of balanced mini-batch logic followed by real-time image augmentation with the algorithm level method of designing new loss function. The training dataset includes 24,530 dermoscopic images of seven skin disease categories, which is by far the largest dataset of skin cancer. The performance metrics of six proposed methods are evaluated on a test dataset of 2,453 images. Our proposed EfficientNetB4-CLF model achieves the highest accuracy of 89.97% and also the highest mean recall of 86.13% with the smallest recalls’ standard deviations of 7.60. The experimental results indicate that our hybrid method is highly effective in training the Deep CNN network on the skin-disease imbalanced dataset. It addresses the problem of slow learning of the minority classes in the networks by combining the data level method of balanced mini-batch logic followed by the real-time image augmentation with the algorithm level method of the newly designed loss function