Advancing of photonics, aided with fruitful and abundant experimental and theoretical studies, over the last four decades has brought about the invention of a large variety of lasers. Among them one of the most popular types is a fiber laser, which is a variation of the standard solid-state laser, with the medium being a clad fiber waveguide structure and different dopants inside core serve as a gain media. They were derived from erbium-doped fiber amplifiers, which are still important component for telecommunications. Since discovery, fiber laser has become a natural choice for many uses, primarily because of the physical characteristics of fiber waveguide structure. Their rapid progress may show how excellent they really are. Although fiber lasers are today widely used in various research and industrial areas, one of the most meaningful applications of fiber laser technology has been through its use in medicine. A wide variety of wavelengths generated by fiber lasers as well as the diversity of physical mechanisms employed in pulse generation also additionally underpins the flexibility of fiber laser technology. This study is devoted to background technology of fiber lasers in the light of medical applications. Basic physics and theories of optical fibers and their important properties are introduced.

Early diagnosis of breast cancer through periodic screening is a vital ally in the fight for survival. Mammography, recognized as one of the most widely used and cost-effective tools for detecting early signs of asymmetry, calcification, masses, and architectural distortion in breast tissue, plays a significant role in nearly all screening scenarios. However, the interpretation and scoring of mammograms is a complex multi-parameter process that frequently leads to false-positive and false-negative results. This article introduces a new deep-learning-based model that classifies mammograms according to the Breast Imaging Reporting and Data System (BI-RADS) assessment categories. The model is trained on a private dataset, intentionally excluding no BI-RADS categories. A novel deep neural network architecture is employed to more accurately classify breasts, including their boundaries, as regions of interest (ROIs). The ConvNeXt architecture serves as a feature extractor for lower-level features, which are then combined with the layers of a randomly initialized naive inception module to capture higher-level features. Diagnosis is achieved through three experimental tests, yielding accuracy rates ranging from 82.08% to 86.27%. These promising accuracy levels, in comparison to previous studies, can be attributed to a more comprehensive approach to addressing BI-RADS scoring challenges. In addition to pursuing further enhancements in accuracy, future research should consider integrating prior radiology reports to create a more realistic end-to-end computer-aided detection system.

The health of crops is a major challenge for productivity growth in agriculture, with plant diseases playing a key role in limiting crop yield. Identifying and understanding these diseases is crucial to preventing their spread. In particular, greenhouse pepper leaves are susceptible to diseases such as mildew, mites, caterpillars, aphids, and blight, which leave distinctive marks that can be used for disease classification. The study proposes a seven-class classifier for the rapid and accurate diagnosis of pepper diseases, with a primary focus on pre-processing techniques to enhance colour differentiation between green and yellow shades, thereby facilitating easier classification among the classes. A novel algorithm is introduced to improve image vibrancy, contrast, and colour properties. The diagnosis is performed using a modified VGG16Net model, which includes three additional layers for fine-tuning. After initialising on the ImageNet dataset, some layers are frozen to prevent redundant learning. The classification is additionally accelerated by introducing flattened, dense, and dropout layers. The proposed model is tested on a private dataset collected specifically for this study. Notably, this work is the first to focus on diagnosing aphid and caterpillar diseases in peppers. The model achieves an average accuracy of 92.00%, showing promising potential for seven-class deep learning-based disease diagnostics. Misclassifications in the aphid class are primarily due to the limited number of samples available.

The practice of detecting skin cancer is based primarily on a visual examination by a dermatologist, followed by a series of tests for a more accurate diagnosis. The concept “the earlier cancer is detected in its natural history, the more effective the treatment is likely to be" is also valid for skin cancer. Hence, any delayed or missed diagnosis can lead to a more severe clinical stage or, what's worse, death. On the other hand, the lack of biomarkers in clinical use brings about overdiagnosis and unnecessary biopsies. DL-CAD system seems to be an excellent candidate for improving diagnostic accuracy and reducing unnecessary treatments. However, the vast majority of conventional CADs manipulate dermoscopic images, which require not only costly equipment but also time-consuming processing. Despite the difficulties with precision, state-of-the-art DL-CAD systems provide an interpretation using digital images, requiring no expertise in cost-effective dermoscopic image capture and interpretation. Pre-processing methods play a crucial role in solving this problem. This study presents results with regard to pre-processing steps to improve the images to be used in the diagnosis of the 5 most common skin cancer types for the proposed CNN based ResNet50 deep learning model. To the best of our knowledge it is the first time that ResNet50 deep-learning model has been utilized in diagnosis of skin cancer. Cilt kanserini tespit edilmesi öncelikle bir dermatolog tarafından yapılan görsel muayeneye ve ardından daha doğru bir tanı için bir dizi teste dayanmaktadır. "Kanser doğal geçmişinde ne kadar erken tespit edilirse, tedavinin o kadar etkili olması muhtemeldir" kavramı cilt kanseri için de geçerlidir. Bu nedenle, gecikmiş veya kaçırılmış herhangi bir tanı daha ağır bir klinik aşamaya veya daha da kötüsü ölüme yol açabilir. Öte yandan, klinik kullanımda biyomarker eksikliği aşırı tanı ve gereksiz biyopsileri beraberinde getirmektedir. DL-CAD sistemleri tanısal doğruluğu artırmak ve gereksiz tedavileri azaltmak için mükemmel bir aday gibi görünmektedir. Bununla birlikte, geleneksel CAD sistemlerin büyük çoğunluğu, yüksek maliyetli ekipmanın yansıra işlenmesi zaman alan dermoskopik görüntüleri kullanır. Hassasiyet hususundaki zorluklara rağmen, modern DL-CAD sistemleri, dijital görüntüleri kullanarak bir yorumlama sağlar ve uygun maliyetli dermoskopik görüntü yakalama ve yorumlamada uzmanlık gerektirmez. Ön işleme yöntemleri bu sorunun çözümünde çok önemli bir rol oynamaktadır. Bu çalışma, önerilen CNN tabanlı ResNet50 derin öğrenme modeli için en yaygın 5 cilt kanseri türünün teşhisinde kullanılacak görüntülerin iyileştirilmesine yönelik ön işleme adımlarına ilişkin sonuçları sunmaktadır. Bildiğimiz kadarıyla, cilt kanseri tanısında ResNet50 derin öğrenme modeli ilk kez kullanılmıştır.

Geleneksel dermoskopik cihazların pahalı olması nedeniyle ulaşılabilirliğinin düşük olması ve uzman hekimlerin mesleki deneyimlerinin yeterli olmayışı cilt kanserinin erken teşhisinde en engelleyici faktörlerdir. Ancak bu alanda daha önce yapılan çalışmalar, çekim sırasında daha az uzmanlık gerektirdiğinden daha ekonomik ve pratik olan dijital benzerlerinden ziyade ağırlıklı olarak yüksek kaliteli dermoskopik görüntülere odaklanmıştır. Bununla birlikte, gürültünün yüksek varlığı nedeniyle tanıda bunların kullanımı zordur ve bu da zahmetli bir filtreleme işlemine neden olur. Filtrelemeyle ilgili en büyük zorluklardan biri, renk, şekil ve kalınlıktaki büyük farklılıklar nedeniyle kılın temizlenmesidir. Kıl temizliği, teşhisin kendisini sabote edebileceğinden teşhis için çok önemli olan verilerin sıkı bir şekilde korunmasını dikkate alan kapsamlı bir yaklaşım gerektirir. Makalede bu amaç için tasarlanan iki yeni filtrenin ana hatları verilmekte ve bu alanda yaygın olarak kullanılan iki filtreye göre performansları incelenmektedir.