Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

A decagonal-shaped split ring resonator metamaterial based on a wearable or textile-based material is presented in this work. Analysis and comparison of various structure sizes are compared considering a compact 6×6 mm 2 metamaterial unit cell, in particular, where robust transmissionreflection (RTR) and Nicolson-Ross-Weir (NRW) methods have been performed to extract the effective metamaterial parameters. An investigation based on the RTR method indicated an average bandwidth of 1.39 GHz with a near-zero refractive index (NZRI) and a 2.35 GHz bandwidth when considering epsilon negative (ENG) characteristics. On the other hand, for the NRW method, approximately 0.95 GHz of NZRI bandwidth and 2.46 GHz of ENG bandwidth have been observed, respectively. These results are also within the ultrawideband (UWB) frequency range, suggesting that the proposed unit cell structure is suitable for textile UWB antennas, biomedical sensors, related wearable systems, and other wireless body area network communication systems. Keywords-metamaterial, near-zero refractive index (NZRI), negative-index metamaterial, epsilon negative (ENG).

Jaundice is describes as yellow discoloration of the skin and other tissues of a newborn infant. It happens when the bilirubin pigment rises up to 5 mg/dL or 85 μmol/L due the organs just started to developed. 75% of newborns in Malaysia had jaundice in the first week of life. Conventional assessment method is not convenient since it unable to detect all type of human skin colours, caused traumatization and costly. This research work proposed jaundice detection system based on the colour card technique that represent all types of human skin colours. This research does not involve any subject due to parent’s cooperation and ethics limitation. The input data is represented by the colour card shades. It represents random bilirubin colour in patient’s body, all types of human skin colour, and standard reference bilirubin concentration colour. It acquires input data by capturing different type of colour card shades using OPT101 photodiode sensor and USB4000-XR1-ES spectrometer. The input data collected were verified through Anderson-Darling normality test and analysed using Pearson’s correlation coefficient to measure the strength of the association between the two variables for each input sample tested. Based on the correlation results, it shows high correlation value (r=0.997). Percentage error analysis is used to validate the experimental device and shows value of 9.90% which prove this technique is reliable. The significant contributions of this work is the improvement of the accuracy in detecting jaundice level for all type of human skin colours through this system.

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Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use.