Oral cancer is among the top three types of cancers in India which accounts for about 30 percent of all types of cancer. We propose here a portable and cost-effective 3D printed smartphone based bimodal (spectroscopy and imaging) device for detection of oral cancer at an early stage. The device has the ability to perform fluorescence spectroscopy and imaging on a single platform using smartphone as an optical spectrometer and a CMOS camera respectively. A miniature 405 nm laser diode has been used as a source. Fluorescence spectra and images of some known fluorophores such as fluorescein, rhodamine, flavin adenine dinucleotide (FAD) and proto-porphyrin (PpIX) have been recorded using the proposed smartphone-based device for validation. The wavelength resolution of device for spectral measurements is 0.25 nm per pixel in the visible range and for imaging the total area captured at the detector is 1cm2 . Preliminary studies have been performed on patients with oral precancer and cancer to evaluate the efficacy of the proposed system for in-vivo diagnosis of the disease at an early stage.
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
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