The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer

Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too

Machine Vision Approach for Diagnosing Tuberculosis (TB) Based on Computerized Tomography (CT) Scan Images

Tuberculosis is curable, still the world’s second inflectional murderous disease, and ranked 13th (in 2020) by the World Health Organization on the list of leading death causes. One of the reasons for its fatality is the unavailability of modern technology and human experts for early detection. This study represents a precise and reliable machine vision-based approach for Tuberculosis detection in the lung through Symmetry CT scan images. TB spreads irregularly, which means it might not affect both lungs equally, and it might affect only some part of the lung. That’s why regions of interest (ROI’s) from TB infected and normal CT scan images of lungs were selected after pre-processing i.e., selection/cropping, grayscale image conversion, and filtration, Statistical texture features were extracted, and 30 optimized features using F (Fisher) + PA (probability of error + average correlation) + MI (mutual information) were selected for final optimization and only 6 most optimized features were selected. Several supervised learning classifiers were used to classify between normal and infected TB images. Artificial Neural Network (ANN: n class) based classifier Multi-Layer Perceptron (MLP) showed comparatively better and probably best accuracy of 99% with execution time of less than a second, followed by Random Forest 98.83%, J48 98.67%, Log it Boost 98%, AdaBoostM1 97.16% and Bayes Net 96.83%

Efektivitas terapi menulis ekspresif untuk menurunkan kecemasan sosial pada korban kekerasan di Kota Probolinggo

INDONESIA: Rasa cemas merupakan perasaan khawatir yang tidak menentu terhadap suatu kondisi dan seseorang akan cenderung menghindari situasi tersebut dikarenakan adanya tekanan dan merasa tidak nyaman. Kecemasan sosial timbul karena adanya pengalaman traumatik yang dialami seseorang. Pentingnya menurunkan tingkat kecemasan pada korban kekerasan agar individu tersebut dapat hidup dan berinteraksi di lingkungannya dengan nyaman dan aman. Penelitian ini bertujuan untuk mengetahui efektivitas terapi menulis ekspresif untuk mengurangi kecemasan sosial korban kekerasan di Kota Probolinggo. Partisipan dalam penelitian ini adalah korban kekerasan yang berusia 15-18 tahun. Penelitian ini menggunakan metode kuantitatif dengan desain pre-eksperimental dan menggunakan satu kelompok eksperimen saja tanpa adanya kelompok kontrol sebagai pembanding. Alat ukur yang digunakan yaitu skala kecemasan sosial berdarakan pada teori La Greca dan Lopez (1998), yang terdiri dari aspek Ketakutan akan evaluasi negatif, Penghindaran sosial dan rasa tertekan dalam situasi yang baru atau berhubungan dengan orang asing (SAD-New) dan Penghindaran sosial dan rasa tertekan yang dialami secara umum atau dengan orang yang dikenal. (SAD-General) Hasil uji analisis data menggunakan Uji Wilcoxon dengan bantuan IBM SPSS 26 for Windows, diperoleh nilai signifikansi sebesar 0,039 (p<0.05). hal ini menunjukkan bahwa terapi menulis ekspresif efektif untuk menurunkan kecemasan sosial pada korban kekerasan di Kota Probolinggo ENGLISH: Anxiety is a feeling of worry that is uncertain about a condition and someone will tend to avoid the situation due to pressure and feeling uncomfortable. Social anxiety arises because of a traumatic experience experienced by someone. The importance of reducing the level of anxiety in victims of violence so that these individuals can live and interact in their environment comfortably and safely. This study aims to determine the effectiveness of expressive writing therapy to reduce the social anxiety of victims of violence in Probolinggo City. Participants in this study were victims of violence aged 15-18 years. This study used a quantitative method with a pre-experimental design and used only one experimental group without a control group as a comparison. The measurement tool used is the social anxiety scale based on the theory of La Greca and Lopez (1998), which consists of aspects of fear of negative evaluation, social avoidance and feeling depressed in new situations or dealing with strangers (SAD-New) and social avoidance. and feelings of distress experienced in general or with people you know. (SAD-General) The results of the data analysis test using the Wilcoxon Test with the help of IBM SPSS 26 for Windows, obtained a significance value of 0.039 (p <0.05). this shows that expressive writing therapy is effective for reducing social anxiety in victims of violence in Probolinggo City ARABIC: القلق هو شعور بالقلق غير مؤكد بشأن حالة ما ويميل شخص ما إلى تجنب الموقف بسبب الضغط والشعور بعدم الارتياح. ينشأ القلق الاجتماعي بسبب تجربة مؤلمة يعاني منها شخص ما. من المهم تقليل مستوى القلق لدى ضحايا العنف حتى يتمكن هؤلاء الأفراد من العيش والتفاعل في بيئتهم بشكل مريح وآمن. تهدف هذه الدراسة إلى تحديد فعالية العلاج الكتابي التعبيري للحد من القلق الاجتماعي لضحايا العنف في مدينة بروبولينجو المشاركون في هذا البحث كانوا ضحايا عنف تتراوح أعمارهم بين 15-18 سنة. استخدم هذا البحث المنهج الكمي بتصميم ما قبل التجربة واستخدمت مجموعة تجريبية واحدة فقط بدون مجموعة ضابطة كمقارنة. أداة القياس المستخدمة هي مقياس القلق الاجتماعي القائم على نظرية لا غريكا و لوبيز (1998) ، والتي تتكون من جوانب الخوف من التقييم السلبي ، والتجنب الاجتماعي والشعور بالاكتئاب في المواقف الجديدة أو التعامل مع الغرباء (حزين-جديد) و التجنب الاجتماعي ومشاعر الضيق بشكل عام أو مع الأشخاص الذين تعرفهم. (حزين-عامعام) حصلت نتائج اختبار تحليل البيانات باستخدام اختبار وولكوكسون بمساعدة IBM SPSS 26 لنظام التشغيل ويندوز على قيمة معنوية قدرها 0.039 (p <0.05). هذا يدل على أن العلاج الكتابي التعبيري فعال للحد من القلق الاجتماعي لدى ضحايا العنف في مدينة بروبولينج

A Novel Brain Tumor Detection and Coloring Technique from 2D MRI Images

The early automated identification of brain tumors is a difficult task in MRI images. For a long time, continuous research efforts have floated a new idea of replacing different grayscale anatomic regions of diagnostic images with appropriate colors that could overcome the problems being faced by radiologists. The colorization of grayscale images is challenging for enhancing various regions&rsquo; contrasts by transforming grayscale images into high-contrast color images. This study investigates standard solutions in discriminating between normal and abnormal regions by assigning colors to grayscale human brain MR images to differentiate different kinds of tissues. The proposed approach is influenced by connected component and index-based colorization methods for applying colors to different regions and abnormal areas. It is an automated approach that varies its inputs using luminance and pixel matrix values and provides the possible outcome. After segmentation, a specific algorithm is devised to colorize the region-of-interest (ROI) areas, which distinguishes and applies colors to differentiate the regions. Results show that implementing the watershed-based area segmentation method and ROI selection method based on the morphological operation helps identify tissues during processing. Moreover, the colorization approach based on luminance and pixel matrix after segmentation and ROI selection is beneficial due to better PSNR and SSIM values and visible contrast improvement. Our proposed algorithm works with less processing overhead and uses less time than those of the industry&rsquo;s previously used color transfer method

A Novel Brain Tumor Detection and Coloring Technique from 2D MRI Images

The early automated identification of brain tumors is a difficult task in MRI images. For a long time, continuous research efforts have floated a new idea of replacing different grayscale anatomic regions of diagnostic images with appropriate colors that could overcome the problems being faced by radiologists. The colorization of grayscale images is challenging for enhancing various regions’ contrasts by transforming grayscale images into high-contrast color images. This study investigates standard solutions in discriminating between normal and abnormal regions by assigning colors to grayscale human brain MR images to differentiate different kinds of tissues. The proposed approach is influenced by connected component and index-based colorization methods for applying colors to different regions and abnormal areas. It is an automated approach that varies its inputs using luminance and pixel matrix values and provides the possible outcome. After segmentation, a specific algorithm is devised to colorize the region-of-interest (ROI) areas, which distinguishes and applies colors to differentiate the regions. Results show that implementing the watershed-based area segmentation method and ROI selection method based on the morphological operation helps identify tissues during processing. Moreover, the colorization approach based on luminance and pixel matrix after segmentation and ROI selection is beneficial due to better PSNR and SSIM values and visible contrast improvement. Our proposed algorithm works with less processing overhead and uses less time than those of the industry’s previously used color transfer method

Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods

Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur

Lung Nodules Localization and Report Analysis from Computerized Tomography (CT) Scan Using a Novel Machine Learning Approach

A lung nodule is a tiny growth that develops in the lung. Non-cancerous nodules do not spread to other sections of the body. Malignant nodules can spread rapidly. One of the numerous dangerous kinds of cancer is lung cancer. It is responsible for taking the lives of millions of individuals each year. It is necessary to have a highly efficient technology capable of analyzing the nodule in the pre-cancerous phases of the disease. However, it is still difficult to detect nodules in CT scan data, which is an issue that has to be overcome if the following treatment is going to be effective. CT scans have been used for several years to diagnose nodules for future therapy. The radiologist can make a mistake while determining the nodule’s presence and size. There is room for error in this process. Radiologists will compare and analyze the images obtained from the CT scan to ascertain the nodule’s location and current status. It is necessary to have a dependable system that can locate the nodule in the CT scan images and provide radiologists with an automated report analysis that is easy to comprehend. In this study, we created and evaluated an algorithm that can identify a nodule by comparing multiple photos. This gives the radiologist additional data to work with in diagnosing cancer in its earliest stages in the nodule. In addition to accuracy, various characteristics were assessed during the performance assessment process. The final CNN algorithm has 84.8% accuracy, 90.47% precision, and 90.64% specificity. These numbers are all relatively close to one another. As a result, one may argue that CNN is capable of minimizing the number of false positives through in-depth training that is performed frequently

Lung Nodules Localization and Report Analysis from Computerized Tomography (CT) Scan Using a Novel Machine Learning Approach

A lung nodule is a tiny growth that develops in the lung. Non-cancerous nodules do not spread to other sections of the body. Malignant nodules can spread rapidly. One of the numerous dangerous kinds of cancer is lung cancer. It is responsible for taking the lives of millions of individuals each year. It is necessary to have a highly efficient technology capable of analyzing the nodule in the pre-cancerous phases of the disease. However, it is still difficult to detect nodules in CT scan data, which is an issue that has to be overcome if the following treatment is going to be effective. CT scans have been used for several years to diagnose nodules for future therapy. The radiologist can make a mistake while determining the nodule&rsquo;s presence and size. There is room for error in this process. Radiologists will compare and analyze the images obtained from the CT scan to ascertain the nodule&rsquo;s location and current status. It is necessary to have a dependable system that can locate the nodule in the CT scan images and provide radiologists with an automated report analysis that is easy to comprehend. In this study, we created and evaluated an algorithm that can identify a nodule by comparing multiple photos. This gives the radiologist additional data to work with in diagnosing cancer in its earliest stages in the nodule. In addition to accuracy, various characteristics were assessed during the performance assessment process. The final CNN algorithm has 84.8% accuracy, 90.47% precision, and 90.64% specificity. These numbers are all relatively close to one another. As a result, one may argue that CNN is capable of minimizing the number of false positives through in-depth training that is performed frequently

YOLO and residual network for colorectal cancer cell detection and counting

The HT-29 cell line, derived from human colon cancer, is valuable for biological and cancer research applications. Early detection is crucial for improving the chances of survival, and researchers are introducing new techniques for accurate cancer diagnosis. This study introduces an efficient deep learning-based method for detecting and counting colorectal cancer cells (HT-29). The colorectal cancer cell line was procured from a company. Further, the cancer cells were cultured, and a transwell experiment was conducted in the lab to collect the dataset of colorectal cancer cell images via fluorescence microscopy. Of the 566 images, 80 % were allocated to the training set, and the remaining 20 % were assigned to the testing set. The HT-29 cell detection and counting in medical images is performed by integrating YOLOv2, ResNet-50, and ResNet-18 architectures. The accuracy achieved by ResNet-18 is 98.70 % and ResNet-50 is 96.66 %. The study achieves its primary objective by focusing on detecting and quantifying congested and overlapping colorectal cancer cells within the images. This innovative work constitutes a significant development in overlapping cancer cell detection and counting, paving the way for novel advancements and opening new avenues for research and clinical applications. Researchers can extend the study by exploring variations in ResNet and YOLO architectures to optimize object detection performance. Further investigation into real-time deployment strategies will enhance the practical applicability of these models

A Novel Expert System for the Diagnosis and Treatment of Heart Disease

The diagnosis of diseases in their early stages can assist us in preventing life-threatening infections and caring for them better than in the last phase because prevention is better than cure. The death rate can be very high due to the unapproachability of diagnosed patients at an early point. Expert systems help us to defeat the problem mentioned above and enable us to automatically diagnose diseases in their early phases. Expert systems use a fuzzy, rule-based inference engine to provide forward-chain methods for diagnosing the patient. In this research, data have been gathered from different sources, such as a hospital, by performing the test on the patients’ age, gender, blood sugar, heart rate, and ECG to calculate the values. The proposed expert system for medical diagnosis can be used to find minimum disease levels and demonstrate the predominant method for curing different medical diseases, such as heart diseases. In the next step, the diagnostic test at the hospital with the novel expert system, the crisp, fuzzy value is generated for input into the expert system. After taking the crisp input, the expert system starts working on fuzzification and compares it with the knowledge base processed by the inference engine. After the fuzzification, the next step starts with the expert system in the defuzzification process converting the fuzzy sets’ value into a crisp value that is efficient for human readability. Later, the expert physician system’s diagnosis calculates the value by using fuzzy sets, and gives an output to determine the patient’s heart disease. In one case, the diagnosis step was accomplished, and the expert system provided the yield with the heart disease risk level as “low”, “high”, or “risky”. After the expert system’s responsibilities have been completed, the physician decides on the treatment and recommends a proper dose of medicine according to the level the expert system provided after the diagnosis step. The findings indicate that this research achieves better performance in finding appropriate heart disease risk levels, while also fulfilling heart disease patient treatment due to the physicians shortfalls