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End-to-end deep learning approach for Parkinson’s disease detection from speech signals

Quan Changqin, Ren Kang, Luo Zhiwei, Chen Zhonglue, Ling Yun

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 2

556--574

More than 90% of patients with Parkinson’s disease suffer from hypokinetic dysarthria. This paper proposes a novel end-to-end deep learning model for Parkinson’s disease detection from speech signals. The proposed model extracts time series dynamic features using time-distributed two-dimensional convolutional neural networks (2D-CNNs), and then captures the dependencies between these time series using a one-dimensional CNN (1D-CNN). The performance of the proposed model was verified on two databases. On Database-1, the proposed model outperformed expert features-based machine learning models and achieved promising results, showing accuracies of 81.6% on the speech task of sustained vowel /a/ and 75.3% on the speech task of reading a short sentence (/si shi si zhi shi shi zi/) in Chinese. On Database-2, the proposed model was assessed on multiple sound types, including vowels, words, and sentences. An accuracy of up to 92% was obtained on the speech tasks, which included reading simple (/loslibros/) and complex (/viste/) sentences in Spanish. By visualizing the features generated by the model, it was found that the learned time series dynamic features are able to capture the characteristics of the reduced overall frequency range and reduced variability of Parkinson’s disease sounds, which are important clinical evidence for detecting Parkinson’s disease patients. The results also suggest that the low-frequency region of the Mel-spectrogram is more influential and important than the high-frequency region for Parkinson’s disease detection from speech.

A novel end-to-end deep learning approach for cancer detection based on microscopic medical images

Hammad Mohamed, Bakrey Mohamed, Bakhiet Ali, Tadeusiewicz Ryszard, Abd El-Latif Ahmed A., Pławiak Paweł

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

737--748

As a result of late diagnosis, cancer is the second leading cause of death in most countries in the world. Usually, many cases of cancer are diagnosed at an advanced stage, which reduces the chances of recovery from the disease due to the inability to provide appropriate treatment. The earlier cancer is detected, the more effective the treatment can be, especially for incurable cancers, which can result in a shorter life expectancy due to the rapid spread of the disease. The early detection of cancer also greatly reduces the financial consequences of it, as the cost of treating it in its early stages is much lower than in its other stages. Therefore, several previous studies focus on developing computer-aided cancer diagnosis systems (CACDs) that can detect cancer in its earliest stages automatically. In this paper, a novel approach is proposed for cancer detection. The proposed approach is an end-to-end deep learning approach, where the input images are fed directly to the deep model for final decision. In this research, the accuracy of a new deep convolutional neural network (CNN) for cancer detection is explored. The microscopic medical images obtained from the cancer database were used to evaluate our study, which were labelled as normal and abnormal images. The presented model achieved an accuracy of 99.99%, which is the highest accuracy compared with other deep learning models. Finally, the proposed approach would be very useful and effective, especially in low-income countries where referral systems for patients with suspected cancer are often unavailable, resulting in delayed and fragmented care.