Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography review paper comprehensively reviews computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The review is meticulously carried out, adhering closely to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are comprehensively reviewed. Evaluating a deep learning architecture based on self-extracted features for classification tasks demonstrated outstanding performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations’ sustainability development goals.

In this work Raman spectra of normal and dengue infected serum and whole blood were analyzed. In normal whole blood and serum characteristic peaks were observed when excited at 442 and 532 nm. In dengue whole blood and serum all peaks found to be blue shifted with reduced Raman intensity. Dengue whole blood and serum shows two peaks at 1614 and 1750 cm⁻¹ which are due to presence of Immunoglobulin antibodies IgG and IgM. Whole study provides a route of information for diagnosis of dengue viral infection.

The purpose of this study was to investigate the optical properties, absorption coefficient (μ _a ) scattering coefficient (μ _s ) and refractive indices, (n) of HeLa cell line in a suspension of 2% minimum essential medium (MEM) at two different (632.8 and 532.0 nm) wave lengths of laser light. Optical properties were determined with Kubelka Munk Model (KMM) and refractive index measurement was made through minimum angle of deviation method (MAD). We reported μ _a = 8.643 ± 0.187 and 2.348 ± 0.249 cm⁻¹ and μ _s = 5.609 ± 0.287 and 88.166 ± 2.833 cm⁻¹ at 632.8 and 532.0 nm, respectively. Refractive index was found to be 1.332 and 1.312 at 632.8 nm and 532.0 nm, respectively. The discussed results provide a route of information for clinical diagnosis, therapeutic application and dosimetry studies in HeLa and other cell lines.