Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment

Purpose

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

Design/methodology/approach

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

Findings

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

Originality/value

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

Detection and Classification of the Different Stages of Alzheimer’s Disease using Sequential Convolutional Neural Network

Background

Alzheimer's disease (AD) is a degenerative disorder known as mild or significant neurocognitive impairment diagnosed in older people and significantly reduces their quality of life. Memory issues are regularly one of the principal indications of Alzheimer's disease; however, beginning manifestations may vary from one individual to another. Detecting Alzheimer's disease is a time-consuming and challenging task but requires brain imaging reports and human expertise.

Objective

More accurate and early diagnosis is essential for timely treatment and risk reduction. The accurate diagnosis of Alzheimer's disease plays an essential role in inpatient treatment, especially at the disease's early stages, because risk awareness allows the patients to undergo preventive measures even before the occurrence of irreversible brain damage. The main objective of this research is to diagnose Alzheimer's disease in its early stage through Magnetic Resonance Imaging (MRI).

Methodology

To detect and classify the various stages of Alzheimer’s disease from the MRI, a Sequential Convolutional Neural Network (S-CNN) model is proposed, which incorporates the advantage of convolution operation and recurrent operation.

Results

The combination of Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and the Open Access Series of Imaging Studies (OASIS) dataset obtained from the Kaggle is used for training and testing the proposed S-CNN model.

Conclusion

The baseline DenseNet169 model has been fine-tuned and added sequentially to achieve better validation accuracy. From the validation, it is observed that it achieves an accuracy of 88.60% and outperforms other reported methods.

Convolutional block attention gate-based Unet framework for microaneurysm segmentation using retinal fundus images

BACKGROUND

Diabetic retinopathy is a major cause of vision loss worldwide. This emphasizes the need for early identification and treatment to reduce blindness in a significant proportion of individuals. Microaneurysms, extremely small, circular red spots that appear in retinal fundus images, are one of the very first indications of diabetic retinopathy. Due to their small size and weak nature, microaneurysms are tough to identify manually. However, because of the complex background and varied lighting factors, it is challenging to recognize microaneurysms in fundus images automatically.

METHODS

To address the aforementioned issues, a unique approach for MA segmentation is proposed based on the CBAM-AG U-Net model, which incorporates Convolutional Block Attention Module (CBAM) and Attention Gate (AG) processes into the U-Net architecture to boost the extraction of features and segmentation accuracy. The proposed architecture takes advantage of the U-Net's encoder-decoder structure, which allows for perfect segmentation by gathering both high- and low-level information. The addition of CBAM introduces channel and spatial attention mechanisms, allowing the network to concentrate on the most useful elements while reducing the less relevant ones. Furthermore, the AGs enhance this process by selecting and displaying significant locations in the feature maps, which improves a model's capability to identify and segment the MAs.

RESULTS

The CBAM-AG-UNet model is trained on the IDRiD dataset. It achieved an Intersection over Union (IoU) of 0.758, a Dice Coefficient of 0.865, and an AUC-ROC of 0.996, outperforming existing approaches in segmentation accuracy. These findings illustrate the model's ability to effectively segment the MAs, which is critical for the timely detection and treatment of DR.

CONCLUSION

The proposed deep learning-based technique for automatic segmentation of micro-aneurysms in fundus photographs produces promising results for improving DR diagnosis and treatment. Furthermore, our method has the potential to simplify the process of delivering immediate and precise diagnoses.

A systematic review on intracranial aneurysm and hemorrhage detection using machine learning and deep learning techniques

The risk of discovering an intracranial aneurysm during the initial screening and follow-up screening are reported as around 11%, and 7% respectively (Zuurbie et al., 2023) to these mass effects, unruptured aneurysms frequently generate symptoms, however, the real hazard occurs when an aneurysm ruptures and results in a cerebral hemorrhage known as a subarachnoid hemorrhage. The objective is to study the multiple kinds of hemorrhage and aneurysm detection problems and develop machine and deep learning models to recognise them. Due to its early stage, subarachnoid hemorrhage, the most typical symptom after aneurysm rupture, is an important medical condition. It frequently results in severe neurological emergencies or even death. Although most aneurysms are asymptomatic and won't burst, because of their unpredictable growth, even small aneurysms are susceptible. A timely diagnosis is essential to prevent early mortality because a large percentage of hemorrhage cases present can be fatal. Physiological/imaging markers and the degree of the subarachnoid hemorrhage can be used as indicators for potential early treatments in hemorrhage. The hemodynamic pathomechanisms and microcellular environment should remain a priority for academics and medical professionals. There is still disagreement about how and when to care for aneurysms that have not ruptured despite studies reporting on the risk of rupture and outcomes. We are optimistic that with the progress in our understanding of the pathophysiology of hemorrhages and aneurysms and the advancement of artificial intelligence has made it feasible to conduct analyses with a high degree of precision, effectiveness and reliability.