Cancer is one of the leading causes of death worldwide, with millions of new cases diagnosed every year. The key to improving survival rates is early detection, as cancers caught in their initial stages are significantly more treatable. Traditional diagnostic methods, such as biopsies, CT scans, MRIs, and mammograms, have limitations in accuracy, speed, and accessibility.
This is where Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are making a creative impact. AI-driven cancer detection systems are improving accuracy, reducing diagnostic time, and making cancer screening more accessible to populations worldwide. This blog explores how AI is transforming early cancer detection, its history, current advancements, and future potential.
A Brief History of Cancer Detection
Before modern medical imaging, cancer detection relied heavily on physical symptoms and biopsy procedures. By the late 19th and early 20th centuries, X-rays and microscopy became essential tools for identifying abnormal growths. However, misdiagnosis rates were high due to human limitations in analyzing medical images.
Mental health care is an essential component of overall well-being, yet it remains one of the most underserved areas of medicine. The stigma surrounding mental health issues, coupled with limited access to qualified professionals, has created barriers to effective care for millions worldwide. AI-powered chatbots are emerging as a promising solution to bridge these gaps, providing accessible, scalable, and cost-effective mental health support. This blog explores how these innovative tools revolutionize mental health care, their challenges, and their potential future impact.
History of AI in Mental Health Care
The integration of artificial intelligence into mental health care has a rich and evolving history. The journey began in the mid-20th century with the development of early AI programs designed to simulate human conversation. One of the earliest examples was ELIZA, created in the 1960s by computer scientist Joseph Weizenbaum. ELIZA was a rudimentary chatbot that used pattern matching and substitution methodology to simulate a psychotherapist’s responses. While basic by today’s standards, ELIZA demonstrated the potential of conversational AI in providing mental health support.
Managing insurance claims is seen as a complicated and lengthy process. Insurance companies receive numerous claims daily, from vehicle accidents and medical expenses to property damage. Manually handling these claims can result in delays, errors, and fraud. We can use Artificial intelligence to simplify the process.
The Problem with Traditional Claims Management
When you make an insurance claim, here’s what usually happens:
You submit your documents (medical bills, photos of damage, etc.).
The insurance company reviews everything manually—a process that can take weeks.
They assess your claim to determine if it’s valid and how much money should be paid.
The claim is either approved or rejected.
While this process sounds straightforward, it’s full of challenges, such as:
It’s Slow: Manually going through forms, photos, and receipts takes much time.
It’s Expensive: Insurance companies need big teams to process claims.
It’s Prone to Errors: Humans can make mistakes when reviewing claims.
It’s Vulnerable to Fraud: Detecting fake claims is difficult without proper tools.
All these issues make it clear that insurance companies need smarter solutions—and that’s where AI comes into the picture.
During one of the keynote speeches in India, an elderly person asked a question: why don’t we use Sanskrit for coding in AI. Though this question might look very strange to researchers at first it has some deep background to it.
Long back when people were trying to build language translators, the main idea was to have an intermediate language to and from which we could translate to any language. If we build direct translation from a language A to B, there will be too many permutations. Imagine, we have 10 languages, and we will have to build 90 (10*9) such translators. But to come up with an intermediate language, we would just need to encode for every 10 languages and 10 decoders to convert the intermediate language to each language. Therefore, there will be only 20 models in total.
So, it was obvious that there is definitely a need for an intermediate language. The question was what should be the intermediate language. Some scientists proposed that we should have Sanskrit as the intermediate language because it had good definitive grammar. Some scientists thought a programming language that can dynamically be loaded should be better and they designed a programming language such as Lisp. Soon enough, they all realized that both natural languages and programming languages such as Lisp would not suffice for multiple reasons: First, there may not be enough words to represent each emotion in different languages. Second, all of this will have to be coded manually.
The approach that became successful was the one in which we represent the intermediate language as a list of numbers along with a bunch of numbers that represent the context. Also, instead of manually coding the meaning of each word, the idea that worked out was representing a word or a sentence with a bunch of numbers. This approach is fairly successful. This idea of representing words as a list of numbers has brought a revolution in natural language understanding. There is humongous research that is happening in this domain today. Please check GPT-3, Dall-E, and Imagen.
If you subtract woman from Queen and add Man, what should be the result? It should be King, right? This can be easily demonstrated using word embedding.
Queen — woman + man = King
Similarly, Emperor — man + woman = Empress
Yes, this works. Each of these words is represented by a list of numbers. So, we are truly able to represent the meaning of words with a bunch of numbers. If you think about it, we learned the meaning of each word in our mother tongue without using a dictionary. Instead, we figured the meaning out using the context.
In our mind, we have sort of a representation of the word which is definitely not in the form of some other natural language. Based on the same principles, the algorithms also figure out the meaning of the words in terms of a bunch of numbers. It is very interesting to understand how these algorithms work. They work on similar principles to humans. They go through the large corpus of data such as Wikipedia or news archives and figure out the numbers with which each word can be represented. The problem is optimization: come up with those numbers to represent each word such that the distance between the words existing in a similar context is very small as compared to the distance between the words existing in different contexts.
The word Cow is closer to Buffalo as compared to Cup because Cow and buffalo usually exist in similar contexts in sentences.
So, in summary, it is very unreasonable to pursue that we should still be considering a natural language to represent the meaning of a word or sentence.
I hope this makes sense to you. Please post your opinions in the comments.
Backpropagation is considered one of the core algorithms in Machine Learning. It is mainly used in training the neural network. And backpropagation is basically gradient descent. What if we tell you that understanding and implementing it is not that hard? Anyone who knows basic Mathematics and has knowledge of the basics of Python Language can learn this in 2 hours. Let’s get started.
Though there are many high-level overviews of the backpropagation and gradient descent algorithms what I found is that unless one implements these from scratch, one is not able to understand many ideas behind neural networks.
Recently, I came up with an idea for a new Optimizer (an algorithm for training neural network). In theory, it looked great but when I implemented it and tested it, it didn’t turn out to be good.
Some of my learning are:
Neural Networks are hard to predict.
Figuring out how to customize TensorFlow is hard because the main documentation is messy.
Theory and Practical are two different things. The more hands-on you are, the higher are your chances of trying out an idea and thus iterating faster.
I am sharing my algorithm here. Even though this algorithm may not be of much use to you but it would give you ideas on how to implement your own optimizer using Tensorflow Keras.
A neural network is basically a set of neurons connected to input and output. We need to adjust the connection strengths such that it gives the least error for a given set of input. To adjust the weight we use the algorithms. One brute force algorithm could be to try all possible combinations of weights (connections strength) but that will be too time-consuming. So, we usually use the greedy algorithm most of these are variants of Gradient Descent. In this article, we will write our custom algorithm to train a neural network. In other words, we will learn how to write our own custom optimizer using TensorFlow Keras.
CloudxLab conducted a successful webinar on “Introduction to Machine Learning” on the 15th of October, 2019. It was a 2-hour session in which the instructor explained the concepts based on Understanding Computer Vision with Deep Learning.
More than 250 learners around the globe attended the webinar. The participants were from countries namely; United States, Canada, Australia, Indonesia, India, Thailand, Philippines, Malaysia, Macao, Japan, Hong Kong, Singapore, United Kingdom, Saudi Arabia, Nepal, & New Zealand.
Every day the world is advancing into the new level of industrialization and this has resulted in the production of a vast amount of data. And, at initial stages, people started considering it as a bane, but later they found out that it’s a boon. So, they started using this data in a productive way. Big data and machine learning are terminologies based on the concept of analyzing and using the same data. Let’s get into more details.
RACE360, an Emerging Technology Conference 2019 (Powered by The Times of India) is happening on Wed, Aug 28th at The Lalit Ashok, Bengaluru. It is presented by REVA University, Bengaluru (REVA Academy for Corporate Excellence (RACE)).
The emergence of Artificial Intelligence has played an essential role in revolutionizing the technical industry. According to many people, Artificial Intelligence is something that makes their work easy; well, it is just one of the qualities of Artificial Intelligence.
What is Artificial Intelligence?
According to Wikipedia, Artificial Intelligence “is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans.”
Artificial intelligence can be categorized into several stages, depending upon the role they play. In this article, we will go through all of these stages, including their real-world application.
