Transfer Learning in Medical Image Classification: Challenges and Opportunities

Transfer Learning is currently popular in Medical Image classification. Transfer Learning methods are extensively applied with CNN’s such as Res-net, Densenet, VGG16, Inception, etc. for various medical diagnosis tasks. CNN’s are around since the 1980s, but 60-80 percent of the TL research in MIC is done in the last three years. While CNN’s can be traditionally used as they are, they have been ensembled, segmented and improvised in recent days to resolve multiple MIC problems. This Review identified three main challenges in implementing Transfer Learning for Medical Image Classification (1) Overparameterization of deep CNN’s (2) Expensive Computations and (3) Insufficient availability of labeled data in the Medical field. The study also identified the opportunities in the form of Light-weight architectures and Multi-stage Transfer Learning which could potentially mitigate the above-mentioned challenges

Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithm

A large percentage of people globally suffer from chronic kidney disease (CKD), a serious health concern. Effective diagnosis, treatment, and referral of CKD depend heavily on early identification and prediction of the disease. However, it is difficult to evaluate and derive significant insights from health data due to its vast and complicated nature. Engineers and medical researchers are using data mining techniques and machine learning algorithms to create predictive models for chronic kidney disease (CKD) in an effort to address this issue. The goal of this research is to create and validate predictive models for chronic kidney disease (CKD) based on a variety of clinical factors, including albuminuria, age, diet, eGFR, and pre-existing medical problems. The objective is to estimate the likelihood of renal failure, which may necessitate kidney dialysis or a transplant, and to evaluate the degree of kidney disease. With the use of this knowledge, patients and healthcare providers should be able to make well-informed decisions about diagnosis, treatment, and lifestyle changes. Patterns in the gathered data can be found, and future incidence of CKD or other related diseases can be predicted, by utilising MLT such as ANN and data mining techniques. Finding novel characteristics linked to the onset of renal disease and adding more trustworthy data from CKD patients. The best algorithm to categorise the data as CKD or NOT_CKD is chosen throughout the design process, and the data is then classified according to this differentiation. Estimated glomerular filtration rate (eGFR), which offers important details about the patient's current kidney function, is used to classify cases of chronic kidney disease. By combining complete patient data with machine learning algorithms, this research advances the diagnosis of chronic kidney disease (CKD) and improves patient outcomes

Design of a Skin Cancer Diagnosing Web Application Based on Convolutional Neural Network Model and Chatterbot Application Programming Interface

Skin cancer has become a great concern for people's wellness. With the popularization of machine learning, a considerable amount of data about skin cancer has been created. However, applications on the market featuring skin cancer diagnosis have barely utilized the data. In this paper, we have designed a web application to diagnose skin cancer with the CNN model and Chatterbot API. First, the application allows the user to upload an image of the user's skin. Next, a CNN model is trained with a huge amount of pre-taken images to make predictions about whether the skin is affected by skin cancer, and if the answer is yes, which kind of skin cancer the uploaded image can be classified. Last, a chatbot using the Chatterbot API is trained with hundreds of answers and questions asked and answered on the internet to interact with and give feedback to the user based on the information provided by the CNN model. The application has achieved significant performance in making classifications and has acquired the ability to interact with users. The CNN model has reached an accuracy of 0.95 in making classifications, and the chatbot can answer more than 100 questions about skin cancer. We have also done a great job on connecting the backend based on the CNN model as well as the Chatterbot API and the frontend based on the VUE Javascript framework