Cervical cancer ranks as the fourth most prevalent cancer globally, emphasizing the critical need for early detection, which is vital for effective treatment. Traditional diagnostic methods have shown limitations in detecting the progression of the disease. Optical techniques, known for their high sensitivity and specificity, are emerging as reliable tools, especially in cancer-related applications. Among these techniques, fluorescence spectroscopy is one of the highly sensitive approaches for identifying biochemical changes that occur during the advancement of cancer. In our study, fluorescence spectral data was collected from human cervix from a diverse group of individuals using a portable smartphone-based fluorescence spectroscopy device. The spectral signals were processed by initially breaking them down into Fourier Bessel series (FBS) coefficients. Subsequently, the Hessian locally linear embedding (HLLE) based dimensionality reduction method was applied to the FBS coefficients, followed by the implementation of a 1D convolutional neural network classifier. The combination of polarized fluorescence spectra acquired from the device and the proposed classification approach has shown promising results, thus it is proven to be a minimally invasive method with the capability to provide real-time diagnoses for patients
Cervical cancer is of significant health concern globally, particularly in developing countries where access to advanced healthcare facilities and medical resources is limited, leading to increased mortality rates. The gold standard for diagnosing CIN (cervical intraepithelial neoplasia) and invasive cervical cancer involves performing a colposcopy-guided biopsy followed by a pathological diagnosis. However, its effectiveness is challenged by limited sensitivity in distinguishing between various stages of cervical cancer, especially in regions where there is a shortage of skilled colposcopists and insufficient access to medical resources. This study presents a method for categorizing infectious, pre-cancerous, and cancerous conditions through the application of multifractal analysis, specifically two dimensional multifractal detrended fluctuation analysis (2D MFDFA), using images obtained through colposcopy. The utilization of multifractal parameters, namely the generalized Hurst exponent and the width of the singularity spectrum, in the analysis distinctly demonstrated variations among the infectious, precancerous, and cancerous conditions. Therefore, it offers valuable insights to healthcare professionals, assisting in the accurate classification and effective management of cervical cancer using Hurst exponent and multifractal spectrum width.
Oral cancer is one of the deadliest diseases around the world with varied morphological traits, hence making it difficult to manually achieve accurate classification. Further, the traditional methods of diagnosis used by clinicians can be time-consuming and prone to error. Therefore, computer-assisted histopathological image classification is of extreme importance for the detection of oral cancer. We propose an image classification model known as External Attention Transformer model based on external attention mechanism, aiming to extract discriminating fine features from oral cancer tissue sections and their normal counterparts. We have used 4946 oral histopathological images classified into two categories: normal and oral squamous cell carcinoma (OSCC). Of the total images, 2435 of them are categorized as normal and 2511 as OSCC. External attention based deep neural network model attained 96.97% classification accuracy. Sensitivity and specificity were recorded as 97.61% and 96.41% respectively. It is found that the effectiveness of artificial intelligence methods for classifying oral cancer has significantly improved in comparison to leading edge methods, and this has a potential for early oral cancer detection.
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