Automatic classification of cervical Pap smear images plays a key role in computer-aided cervical cancer diagnosis. Conventional classification approaches rely on cell segmentation and feature extraction methods. Due to overlapping cells, dust, impurities and uneven irradiation, the accurate segmentation and feature extraction of Pap smear images are still challenging. To overcome the difficulties of the feature-based approaches, deep learning is becoming more important alternative. Since the number of cervical cytological images is limited, an adaptive pruning deep transfer learning model (PsiNet-TAP) is proposed for Pap smear images classification. We designed a novel network to classify Pap smear images. Due to the limited number of images, we adopted transfer learning to obtain the pre-trained model. Then it was optimized by modifying the convolution layer and pruning some convolution kernels that may interfere with the target classification task. The proposed method PsiNet-TAP was tested on 389 cervical Pap smear images. The method has achieved remarkable performance (accuracy: more than 98%), which demonstrates the strength of the proposed method for providing an efficient tool for cervical cancer classification in clinical settings.

The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.